Diagnosis, prognosis and identification of potential therapeutic targets of multiple myeloma based on gene expression profiling

ABSTRACT

Gene expression profiling between normal B cells/plasma cells and multiple myeloma cells revealed four distinct subgroups of multiple myeloma plasma cells that have significant correlation with clinical characteristics known to be associated with poor prognosis. Diagnosis for multiple myeloma (and possibly monoclonal gammopathy of undetermined significance) based on differential expression of 14 genes, as well as prognostics for the four subgroups of multiple myeloma based on the expression of 24 genes were also established. Gene expression profiling also allows placing multiple myeloma into a developmental schema parallel to that of normal plasma cell differentiation. The development of a gene expression- or developmental stage-based classification system for multiple myeloma would lead to rational design of more accurate and sensitive diagnostics, prognostics and tumor-specific therapies for multiple myeloma.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This is a continuation-in-part of U.S. Ser. No. 10/409,004, filedApr. 8, 2003, which is a continuation-in-part of U.S. Ser. No.10/289,746, filed Nov. 7, 2002, which claims benefit of provisionalpatent applications U.S. S No. 60/348,238, filed Nov. 7, 2001, U.S. SNo. 60/355,386, filed Feb. 8, 20002, and U.S. S No. 60/403,075, filedAug. 13, 2002, which are all abandoned now.

FEDERAL FUNDING LEGEND

[0002] This invention was produced in part using funds obtained througha grant from the National Cancer Institute. Consequently, the federalgovernment has certain rights in this invention.

BACKGROUND OF THE INVENTION

[0003] 1. Field of the Invention

[0004] The present invention relates generally to the field of cancerresearch. More specifically, the present invention relates to geneexpression profiling of plasma cells from patients with multiple myelomaor monoclonal gammopathy of undetermined significance.

[0005] 2. Description of the Related Art

[0006] Multiple myeloma (MM) is a uniformly fatal tumor of terminallydifferentiated plasma cells (PCs) that home to and expand in the bonemarrow. Although initial transformation events leading to thedevelopment of multiple myeloma are thought to occur at a post-germinalcenter stage of development as suggested by the presence of somatichypermutation of IGV genes, progress in understanding the biology andgenetics of and advancing therapy for multiple myeloma has been slow.

[0007] Multiple myeloma cells are endowed with a multiplicity ofanti-apoptotic signaling mechanisms that account for their resistance tocurrent chemotherapy and thus the ultimately fatal outcome for mostpatients. While aneuploidy by interphase fluorescence in situhybridization (FISH) and DNA flow cytometry are observed in >90% ofcases, cytogenetic abnormalities in this typically hypoproliferativetumor are informative in only about 30% of cases and are typicallycomplex, involving on average 7 different chromosomes. Given this“genetic chaos” it has been difficult to establish correlations betweengenetic abnormalities and clinical outcomes. Only recently haschromosome 13 deletion been identified as a distinct clinical entitywith a grave prognosis. However, even with the most comprehensiveanalysis of laboratory parameters, such as β2-microglobulin (β2M),C-reactive protein (CRP), plasma cell labeling index (PCLI), metaphasekaryotyping, and FISH, the clinical course of patients afflicted withmultiple myeloma can only be approximated, because no more than 20% ofthe clinical heterogeneity can be accounted for. Thus, there aredistinct clinical subgroups of multiple myeloma and modern moleculartests may identify these entities.

[0008] Monoclonal gammopathy of undetermined significance (MGUS) andmultiple myeloma are the most frequent forms of monoclonal gammopathies.Monoclonal gammopathy of undetermined significance is the most commonplasma cell dyscrasia with an incidence of up to 10% of population overage 75. The molecular basis of monoclonal gammopathy of undeterminedsignificance and multiple myeloma are not very well understood and it isnot easy to differentiate the two disorders. The diagnosis of multiplemyeloma or monoclonal gammopathy of undetermined significance isidentical in ⅔ of cases using classification systems that are based on acombination of clinical criteria such as the amount of bone marrowplasmocytosis, the concentration of monoclonal immunoglobulin in urineor serum, and the presence of bone lesions. Especially in early phasesof multiple myeloma, the differential diagnosis is associated with acertain degree of uncertainty.

[0009] Furthermore, in the diagnosis of multiple myeloma, the clinicianmust exclude other disorders in which a plasma cell reaction may occursuch as rheumatoid arthritis and connective tissue disorders, ormetastatic carcinoma where the patient may have osteolytic lesionsassociated with bone metastases. Therefore, given that multiple myelomais thought to have an extended latency and clinical features arerecognized many years after the development of the malignancy, newmolecular diagnostic techniques are needed in screening for the diseaseand provide differential diagnosis for multiple myeloma, e.g.,monoclonal gammopathy of undetermined significance versus multiplemyeloma or the recognition of various subtypes of multiple myeloma.

[0010] Thus, the prior art is deficient in methods of differentialdiagnosing and identifying distinct and prognostically relevant clinicalsubgroups of multiple myeloma. The present invention fulfills thislong-standing need and desire in the art.

SUMMARY OF THE INVENTION

[0011] Bone marrow plasma cells from 74 patients with newly diagnosedmultiple myeloma, 5 with monoclonal gammopathy of undeterminedsignificance (MGUS), and 31 normal volunteers (normal plasma cells) werepurified by CD138⁺ selection. Gene expression of purified plasma cellsand 7 multiple myeloma cell lines were profiled using high-densityoligonucleotide microarrays interrogating ˜6,800 genes. On hierarchicalclustering analysis, normal and multiple myeloma plasma cells weredifferentiated and four distinct subgroups of multiple myeloma (MM1,MM2, MM3 and MM4) were identified. The expression patterns of MM1 wassimilar to normal plasma cells and monoclonal gammopathy of undeterminedsignificance, whereas MM4 was similar to multiple myeloma cell lines.Clinical parameters linked to poor prognosis such as abnormal karyotype(P=0.0003) and high serum β2-microglobulin levels (P=0.0004) were mostprevalent in MM4. Genes involved in DNA metabolism and cell cyclecontrol were overexpressed in a comparison of MM1 and MM4.

[0012] Using chi square and Wilcoxon rank sum tests, 120 novel candidatedisease genes that discriminated between normal and malignant plasmacells (P<0.0001) were identified. Many of these candidate genes areinvolved in adhesion, apoptosis, cell cycle, drug resistance, growtharrest, oncogenesis, signaling and transcription. In addition, a totalof 156 genes, including FGFR3 and CCND1, exhibited highly elevated(“spiked”) expression in at least 4 of the 74 multiple myeloma cases(range of spikes: 4 to 25). Elevated expression of FGFR3 and CCND1 werecaused by the translocation t(4;14)(p16;q32) or t(11;14)(q13;q32).

[0013] The present invention also identifies, through multivariatestepwise discriminant analysis, a minimum subset of genes whoseexpression is intimately associated with the malignant features ofmultiple myeloma. Fourteen genes were defined as predictors that areable to differentiate plasma cells of multiple myeloma patients fromnormal plasma cells with a high degree of accuracy, and 24 genes wereidentified as predictors that are able to differentiate the distinctsubgroups of multiple myeloma (MM1, MM2, MM3 and MM4) described herein.

[0014] Furthermore, data disclosed herein indicated that multiplemyeloma can be placed into a developmental schema parallel to that ofnormal plasma cell differentiation. Based on gene expression profiling,the MM4, MM3 and MM2 subgroups described above were found to havesimilarity with tonsil B cells, tonsil plasma cells and bone marrowplasma cells respectively. These data suggest that the enigmaticmultiple myeloma is amendable to a gene expression/developmentstage-based classification system.

[0015] The present invention of gene expression profiling using DNAmicroarray and hierarchical clustering analysis can be used to classifysubgroups of multiple myeloma, identify genes with differentialexpression in subsets of multiple myeloma patients, and identifypotential therapeutic targets for multiple myeloma. For example, thereare provided methods of diagnosis for multiple myeloma or subgroups ofmultiple myeloma based on the expression of a group of 14 genes or agroup of 24 genes respectively.

[0016] In another aspect of the present invention, there are providedmethods of treatment for multiple myeloma. Such methods involveinhibiting or enhancing the expression of genes that are found to beover-expressed or down-regulated respectively in multiple myelomapatients as disclosed herein.

[0017] The present invention also provides a method of developmentalstage-based classification for multiple myeloma that is based on geneexpression profiling between multiple myeloma cells and normal B orplasma cells and the present invention also provides a method ofcontrolling bone loss in an individual, comprising the step ofinhibiting the expression of the DKK1 gene (accession number NM012242)or the activity of the protein expressed by the DKK1 gene.

[0018] Other and further aspects, features, and advantages of thepresent invention will be apparent from the following description of thepresently preferred embodiments of the invention. These embodiments aregiven for the purpose of disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1A shows cluster-ordered data table. The clustering ispresented graphically as a colored image. Along the vertical axis, theanalyzed genes are arranged as ordered by the clustering algorithm. Thegenes with the most similar patterns of expression are placed adjacentto each other. Likewise, along the horizontal axis, experimental samplesare arranged; those with the most similar patterns of expression acrossall genes are placed adjacent to each other. Both sample and genegroupings can be further described by following the solid lines(branches) that connect the individual components with the largergroups. The color of each cell in the tabular image represents theexpression level of each gene, with red representing an expressiongreater than the mean, green representing an expression less than themean, and the deeper color intensity representing a greater magnitude ofdeviation from the mean.

[0020]FIG. 1B shows an amplified gene cluster showing genesdownregulated in MM. Most of the characterized and sequence-verifiedcDNA-encoded genes are known to be immunoglobulins.

[0021]FIG. 1C shows a cluster enriched with genes whose expression levelwas correlated with tumorigenesis, cell cycle, and proliferation rate.Many of these genes were also statistically significantly upregulated inmultiple myeloma (χ² and WRS test) (see Table 5).

[0022]FIG. 1D shows a dendrogram of hierarchical cluster. 74 cases ofnewly diagnosed untreated multiple myeloma, 5 monoclonal gammopathy ofundetermined significance, 8 multiple myeloma cell lines, and 31 normalbone marrow plasma cell samples clustered based on the correlation of5,483 genes (probe sets). Different-colored branches represent normalplasma cell (green), monoclonal gammopathy of undetermined significance(blue arrow), multiple myeloma (tan) and multiple myeloma cell lines(brown arrow).

[0023]FIG. 1E shows a dendrogram of a hierarchical cluster analysis of74 cases of newly diagnosed untreated multiple myeloma alone(clustergram note shown). Two major branches contained two distinctcluster groups. The subgroups under the right branch, designated MM1(light blue) and MM2 (blue) were more related to the monoclonalgammopathy of undetermined significance cases in FIG. 1D. The twosubgroups under the left branch, designated MM3 (violet) and MM4 (red)represent samples that were more related to the multiple myeloma celllines in FIG. 1D.

[0024]FIG. 2 shows the spike profile distributions of FGFR3, CST6,IFI27, and CCND1 gene expression. The normalized average difference (AD)value of fluorescence intensity of streptavidin-phycoerythrin stainedbiotinylated cRNA as hybridized to probes sets is on the vertical axisand samples are on the horizontal axis. The samples are ordered fromleft to right: normal plasma cells (NPCs) (green), MM1 (light blue), MM2(dark blue), MM3 (violet), and MM4 (red). Note relatively low expressionin 31 plasma cells and spiked expression in subsets of multiple myelomasamples. The P values of the test for significant nonrandom spikedistributions are noted.

[0025]FIG. 3A shows a GeneChip HuGeneFL analysis of MS4A2 (CD20) geneexpression. The normalized average difference (AD) value of fluorescenceintensities of streptavidin-phycoerythrin stained biotinylated cRNA ashybridized to two independent probes sets (accession numbers M27394(blue) and X12530 (red) located in different regions of the MS4A2 geneis on the vertical axis and samples are on the horizontal axis. Noterelatively low expression in 31 normal plasma cells (NPCs) and spikedexpression in 5 of 74 multiple myeloma samples (multiple myeloma plasmacells). Also note similarity in expression levels detected by the twodifferent probe sets.

[0026]FIG. 3B shows immunohistochemistry for CD20 expression on clonalmultiple myeloma plasma cells: (1) bone marrow biopsy section showingasynchronous type multiple myeloma cells (H&E, ×500); (2) CD20⁺ multiplemyeloma cells (×100; inset ×500); (3) biopsy from a patient with mixedasynchronous and Marschalko-type multiple myeloma cells (H&E, ×500); and(4) CD20⁺ single lymphocyte and CD20⁻ multiple myeloma cells (×200).CD20 immunohistochemistry was examined without knowledge of clinicalhistory or gene expression findings.

[0027]FIG. 4 shows the gene expression correlates with proteinexpression. Gene and protein expression of CD markers known to bedifferentially expressed during B-cell differentiation were comparedbetween the multiple myeloma cell line CAG (left panel) and theEpstein-Barr virus (EBV) transformed B-lymphoblastoid line ARH-77 (rightpanel). In both panels, the 8 CD markers are listed in the left columnof each panel. Flow cytometric analysis of protein expression ispresented in the second column; the average difference (AD) and absolutecall (AC) values of gene expression are presented in the third andfourth columns. Note the strong expression of both the gene and proteinfor CD138 and CD38 in the CAG cells but the low expression in the ARH-77cells. The opposite correlation is observed for the remaining markers.

[0028]FIG. 5 shows multivariate discriminant analysis of 14 features ofall normal plasma cells, MMs, monoclonal gammopathy of undeterminedsignificance and multiple myeloma cell lines. This scatterplot resultedfrom the orthogonal projection of value per case onto the plane definedby the 2 centers. The green plots represent normal plasma cells; theblue plots represent multiple myeloma plasma cells and multiple myelomacell lines; the pink plots represent monoclonal gammopathy ofundetermined significance.

[0029]FIG. 6A shows 269 cases of multiple myeloma, 7 multiple myelomacell lines, 7 monoclonal gammopathy of undetermined significance and 32normal plasma cells samples clustered based on the correlation of 5,483genes (probe sets). Two major branches contained two distinct clustergroups. The subgroup including normal plasma cell samples contained 1monoclonal gammopathy of undetermined significance (green arrow) and 2misclassified multiple myeloma samples (pink arrow). FIG. 6B showsamplified sample cluster showing samples connecting to the normal group.

[0030]FIG. 7 shows multivariate discriminant analysis of 24 features ofall multiple myeloma, monoclonal gammopathy of undetermined significanceand multiple myeloma cell lines. This scatterplot resulted from theorthogonal projection of value per case onto the plane defined by the 4centers. The red plots represent the MM1 subgroup; the green plotsrepresent the MM2 subgroup; the blue plots represent the MM3 subgroup;and the pink plots represent the MM4 subgroup; the light blue plots areungroup cases; and the large yellow plots represent the group centers.

[0031]FIG. 8A shows endothelin B receptor (ENDBR) expression in normalplasma cells and in approximately 200 myeloma patients starting with P1through P226 as indicated by the mean fluorescent intensity of themicroarry data depicted on the Y axis. FIG. 8B shows endothelin Breceptor expression in normal plasma cells and in newly diagnosedmyeloma patients.

[0032]FIG. 9A shows the expression of endothelin B receptor (ENDBR) infeeder cells and myeloma cells P323 and P322 before and afterco-culture. FIG. 9B shows the expression of endothelin 1 in feeder cellsand myeloma cells P323 and P322 before and after co-culture.

[0033]FIG. 10 shows flow cytometric, immunofluorescence and cytologicalanalysis of normal B cell and plasma cell samples.

[0034] CD19-Selected Tonsil B cells: Tonsil-derived mononuclearfractions were tested for percentage of tonsil B cells prior toanti-CD19 immunomagnetic bead sorting by using two-color FACs analysiswith antibodies to CD20/CD38 (a and b). The post-sorting purity of thetonsil B cell sample was determined by CD20/CD38 (c and d), CD138/CD20(e), and CD138/CD38 (f) staining. Cytospin preparations of the purifiedtonsil B cell samples were stained with Wright Giemsa and cellmorphology observed with light microscopy (g). Purifed B cells were alsostained with AMCA and FITC antibodies against cytoplasmic immunoglobulin(cIg) light chain κ and λ and observed by immunofluorescence microscopy(h). Note the lack of cIg staining and thus minimal plasma cellcontamination in the tonsil B cell fraction.

[0035] CD 138-Selected Tonsil Plasma Cells: Tonsil mononuclear fractionswere tested for percentage of plasma cells prior to anti-CD138immunomagnetic bead sorting by using two color FACs analysis usingantibodies to CD38/CD45 (i) and CD138/CD45 (j). The post-sorting purityof the tonsil plasma cell samples was determined by dual color FACsanalysis of CD38/CD45 (k), CD138/CD45 (l), CD38/CD20 (m), and CD138/CD38(n). Cytospin preparations of the purified tonsil plasma cells wereanalyzed for morphological appearance (o) and cIg (p).

[0036] CD 138-Selected Bone Marrow Plasma Cells: Mononuclear fractionsfrom bone marrow aspirates were tested for percentage of plasma cellsprior to anti-CD138 immunomagnetic bead sorting by using two color FACsanalysis using antibodies to CD38/CD45 (q) and CD138/CD45 (r). The postsorting purity of the bone marrow plasma cell sample was determined bydual color FACs analysis of CD38/CD45 (s), CD138/CD45 (t), CD38/CD20(u), and CD138/CD38 (v). Cytospin preparations of the purified bonemarrow plasma cells were analyzed for morphological appearance (w) andcIg (x). Note the high percentage of cIg-positive bone marrow plasmacells with clear plasma cell morphologic characteristics.

[0037]FIG. 11 shows two-dimensional hierarchical cluster analysis ofnormal human plasma cells. Included were 7 tonsil BC (TBC), 7 tonsil PC(TPC), and 7 bone marrow PC (BPC) samples clustered based on thecorrelation of experimental expression profiles of 4866 probe sets. Theclustering is presented graphically as a colored image. Along thevertical axis, the analyzed genes are arranged as ordered by theclustering algorithm. The genes with the most similar patterns ofexpression are placed adjacent to each other. Experimental samples aresimilarly arranged in the horizontal axis. The color of each cell in thetabular image represents the expression level of each gene, with redrepresenting an expression greater than the mean, green representing anexpression less than the mean, and the deeper color intensityrepresenting a greater magnitude of deviation from the mean. The topdendrogram produces two major branches separating tonsil BCs from PCs.In addition, within the PC cluster, tonsil PCs and bone marrow PCs areseparated on three unique branches.

[0038]FIG. 12 shows two-dimensional hierarchical cluster analysis ofexperimental expression profiles and gene behavior of 30 EDG-MM. Bcells, tonsil and bone marrow plasma cells, and multiple myeloma (MM)samples were analyzed using a cluster-ordered data table. The tonsil Bcell, tonsil plasma cell, bone marrow plasma cell samples are indicatedby red, blue, and golden bars respectively. The nomenclature for the 74MM samples is as indicated in Zhan et al. (2002a). Along the verticalaxis, the analyzed genes are arranged as ordered by the clusteringalgorithm. The genes with the most similar patterns of expression areplaced adjacent to each other. Both sample and gene groupings can befurther described by following the solid lines (branches) that connectthe individual components with the larger groups. The tonsil B cellcluster is identified by the horizontal red bar. The color of each cellin the tabular image represents the expression level of each gene, withred representing an expression greater than the mean, green representingan expression less than the mean, and the deeper color intensityrepresenting a greater magnitude of deviation from the mean.

[0039]FIG. 13 shows two-dimensional hierarchical cluster analysis ofexperimental expression profiles and gene behavior of 50 LDG-MM1 genes.Genes are plotted along the vertical axis (right side), and experimentalsamples are plotted along the top horizontal axis by their similarity.The tonsil plasma cell cluster is identified by a horizontal blue bar.Tonsil B cell, tonsil plasma cell, and bone marrow plasma cell samplesare indicated as in FIG. 12.

[0040]FIG. 14 shows two-dimensional hierarchical cluster analysis ofexperimental expression profiles and gene behavior of 50 LDG-MM2 genes.Genes are plotted along the vertical axis (right side), and experimentalsamples are plotted along the top horizontal axis by their similarity.The bone marrow plasma cell cluster is identified by a horizontal goldenbar. Tonsil B cell, tonsil plasma cell, and bone marrow plasma cellsamples are indicated as in FIG. 12.

[0041]FIG. 15 shows variation in expression of proliferation genesreveals similarities between tonsil B cells and MM4. The data are shownas boxplot of Kruskal-Wallis test values. The seven groups analyzed(tonsil B cells, tonsil plasma cells, bone marrow plasma cells, and geneexpression defined subgroups MM1, MM2, MM3, and MM4) are distributedalong the x-axis and the natural log transformed average difference isplotted on the y axis. EZH2; P=7.61×10⁻¹¹; KNSL1, P=3.21×10⁻⁸; PRKDC,P=2.86×10⁻¹¹; SNRPC, P=5.44×10⁻¹²; CCNB1, P=2.54×10⁸; CKS2,P=9.49×10⁻¹¹; CKS1, P=5.86×10⁻⁹; PRIM1, P=4.25×10⁻⁵.

[0042]FIG. 16 shows the receiver operating characteristic (ROC) curvesfor the multiple myeloma (MM) vs monoclonal gammopathy of undeterminedsignificance (MGUS) classification.

[0043]FIG. 17A shows global gene expression patterns reflect bonelesions in multiple myeloma. Clusterview of normalized expression levelsof 58 genes identified by logistic regression and permutation analysisas being significantly differentially expressed in malignant plasmacells from patients with 0 (n=36) and 1+ MRI focal lesions (n=137)(P<0.0001). The 28 genes exhibiting overexpression in plasma cells frompatients with 1+ MRI lesions are ordered from top to bottom based on thesignificance rank. Likewise the 30 genes showing significant elevationin patients with no MRI-lesions are ordered from bottom to top based onsignificance rank. Expression scales range from −3 (blue) to +3 (red) asindicated below the data display. DKK1 ranked 4th in significance.

[0044]FIG. 17B shows Log 2 DKK1 expression was correlated with bonelesions. Box plots of DKK1 gene expression levels in purified plasmacells in relation to lesions detected by MRI, x-ray, and MRI and xray.Odds ratios for relative risk and associated p values are presented inthe text.

[0045]FIG. 18 shows DKK1 protein expression is restricted to plasmacells in the bone marrow. Expression of DKK1 and cytoplasmicimmunoglobulin (cIg) kappa or lambda light chain was examined byimmunohistochemistry (A-D) and immunofluorescence (E-H) staining of bonemarrow biopsies and bone marrow aspirates, respectively. DKK1 proteinexpression in representative examples from a patient with low (A, C) andhigh (B, D) DKK1 gene expression are correlated. A, B, E, and F arestained for cIg; C, D, G, and H are stained for DKK1. Note that bothbiopsies (A and B) stain brightly for cIg, but only the case with highDKK1 gene expression (B) shows DKK1 protein expression (D). All imagesare at 250× magnification. Immunofluorescence staining was performed onmononuclear cells from bone marrow aspirates of a multiple myelomapatient (E and G) and a normal healthy donor (F and H). Note that onlyplasma cells stain for DKK1 in both multiple myeloma and normal bonemarrow (arrows, E and G, indicate neutrophil nuclear morphology). Alsonote that not all plasma cells in normal marrow (F and H) are DKK1positive (see arrows). All images are at 1000× magnification.

[0046]FIG. 19 shows DKK1 protein in the serum is highly correlated withDKK1 gene expression and the presence of bone lesions. FIG. 19A showsthe expression of DKK1 mRNA and proteins detected by microarray andELISA respectively and both of the results were transformed by the logbase 2 and normalized to give a mean of 0 and variance of 1. Each barindicates the relative relationship between each sample. There was astrong significant correlation between DKK1 transcripts and protein(r=0.65, p<0.0001). FIG. 19B shows box plots of bone marrow serum DKK1protein levels in relation to lesions detected by MRI, x-ray, and MRIand x-ray. Odds ratios for relative risk and associated p values arepresented in the text.

[0047]FIG. 20 shows multiple myeloma serum can block alkalinephosphatase production in BMP-2 treated C2C12 cells in a DKK1-dependentmanner. Twenty thousand C2C12 cells were cultured for 5 days in thepresence of 100 ng/ml BMP2, BMP2+10% normal bone marrow serum (NS),BMP2+10% multiple myeloma bone marrow serum from 2 patients (MMS1 andMMS2), or BMP2+10% multiple myeloma patient serum after pre-incubationwith goat anti-DKK1 or goat polyclonal IgG 30 minutes at RT. Alkalinephosphatase levels were determined and each bar represents the mean±SEM)of triplicate determinants. Data were analyzed by Wilcoxon test afterestablishing homogeneity of variances. * Indicates p<0.05 in comparisonto ALP in BMP2+10% normal serum.

DETAILED DESCRIPTION OF THE INVENTION

[0048] There is now strong evidence that global gene expressionprofiling can reveal a molecular heterogeneity of similar or relatedhematopoietic malignancies that have been difficult to distinguish. Themost significantly differentially expressed genes in a comparison ofnormal and malignant cells can be used in the development of clinicallyrelevant diagnostics as well as provide clues into the basic mechanismsof cellular transformation. In fact, these profiles might even be usedto identify malignant cells even in the absence of any clinicalmanifestations. In addition, the biochemical pathways in which theproducts of these genes act may be targeted by novel therapeutics.

[0049] The present invention demonstrates that both normal and malignantplasma cells can be purified to homogeneity from bone marrow aspiratesusing anti-CD138-based immunomagnetic bead-positive selection. Usingthese cells, the present invention provides the first comprehensiveglobal gene expression profiling of newly diagnosed multiple myelomapatients and contrasted these expression patterns with those of normalplasma cells. Novel candidate multiple myeloma disease genes wereidentified using the method of gene expression profiling disclosedherein and this profiling has lead to the development of a gene-basedclassification system for multiple myeloma.

[0050] Results from hierarchical cluster analysis on multiple myelomaand normal plasma cells, as well as the benign plasma cell dyscrasiamonoclonal gammopathy of undetermined significance and theend-stage-like multiple myeloma cell lines revealed normal plasma cellsare unique and that primary multiple myeloma is either like monoclonalgammopathy of undetermined significance or multiple myeloma cell lines.In addition, multiple myeloma cell line gene expression was homogeneousas evidenced by the tight clustering in the hierarchical analysis. Thesimilarity of multiple myeloma cell line expression patterns to primarynewly diagnosed forms of multiple myeloma support the validity of usingmultiple myeloma cell lines as models for multiple myeloma.

[0051] Upon hierarchical clustering of multiple myeloma alone, fourdistinct clinical multiple myeloma subgroups (MM1 to MM4) weredistinguished. The MM1 subgroup contained samples that were more likemonoclonal gammopathy of undetermined significance, whereas the MM4subgroup contained samples more like multiple myeloma cell lines. Themost significant gene expression patterns differentiating MM1 and MM4were cell cycle control and DNA metabolism genes, and the MM4 subgroupwas more likely to have abnormal cytogenetics, elevated serum β2M,elevated creatinine, and deletions of chromosome 13. These are importantvariables that historically have been linked to poor prognosis.

[0052] Gene Expression Changes in Multiple Myeloma

[0053] Data disclosed herein indicated that the MM4 subgroup likelyrepresents the most high-risk clinical entity. Thus, knowledge of themolecular genetics of this particular subgroup should provide insightinto its biology and possibly provide a rationale for appropriatesubtype-specific therapeutic interventions. The most significant geneexpression changes differentiating the MM1 and MM4 subgroups code foractivities that clearly implicate MM4 as having a more proliferative andautonomous phenotype. The most significantly altered gene in thecomparison, TYMS (thymidylate synthase), which functions in theprymidine biosynthetic pathway, has been linked to resistance tofluoropyrimidine chemotherapy and also poor prognosis in colorectalcarcinomas. Other notable genes upregulated in MM4 were the CAAXfarnesyltransferase gene, FTNA. Farnesyltransferase prenylates RAS, apost translational modification required to allow RAS to attach to theplasma membrane. These data suggest that farnesyltransferase inhibitorsmay be effective in treating patients with high levels of FTNAexpression.

[0054] Two other genes coding for components of the proteasome pathway,POH1 (26S proteasome-associated pad1 homolog) and UBL1 (ubiquitin-likeprotein 1) were also overexpressed in MM4. Overexpression of POH1confers P-glycoprotein-independent, pleotropic drug resistance tomammalian cells. UBL1, also known as sentrin, is involved in manyprocesses including associating with RAD51, RAD52, and p53 proteins inthe double-strand repair pathway; conjugating with RANGAP 1 involved innuclear protein import; and importantly for multiple myeloma, protectingagainst both Fas/Apo-1 (TNFRSF6) or TNFR1-induced apoptosis. In contrastto normal plasma cells, more than 75% of multiple myeloma plasma cellsexpress abundant mRNA for the multidrug resistance gene,lung-resistance-related protein (MVP). These data are consistent withprevious reports showing that expression of MVP in multiple myeloma is apoor prognostic factor. Given the uniform development of chemotherapyresistance in multiple myeloma, the combined overexpression of POH1 andMVP may have profound influences on this phenotype. The deregulatedexpression of many genes whose products function in the proteasomepathway may be used in the pharmacogenomic analysis of efficacy ofproteasome inhibitors like PS-341 (Millennium Pharmaceuticals,Cambridge, Mass.).

[0055] Another significantly upregulated gene in MM4 was the singlestranded DNA-dependent ATP-dependent helicase (G22P1), which is alsoknown as Ku70 autoantigen. The DNA helicase II complex, made up of p70and p80, binds preferentially to fork-like ends of double-stranded DNAin a cell cycle-dependent manner. Binding to DNA is thought to bemediated by p70 and dimerization with p80 forms the ATP-dependentDNA-unwinding enzyme (helicase II) and acts as the regulatory componentof a DNA-dependent protein kinase (DNPK) which was also significantlyupregulated in MM4. The involvement of the helicase II complex in DNAdouble-strand break repair, V(D)J recombination, and notably chromosomaltranslocations has been proposed. Another gene upregulated was the DNAfragmentation factor (DFFA). Caspase-3 cleaves the DFFA-encoded 45 kDsubunit at two sites to generate an active factor that produces DNAfragmentation during apoptosis signaling. In light of the many blocks toapoptosis in multiple myeloma, DFFA activation could result in DNAfragmentation, which in turn would activate the helicase II complex thatthen may facilitate chromosomal translocations. It is of note thatabnormal karyotypes, and thus chromosomal translocations, are associatedwith the MM4 subgroup which tended to overexpress these two genes.

[0056] Hence, results disclosed herein demonstrate that directcomparison of gene expression patterns in multiple myeloma and normalplasma cells can identified novel genes that could represent thefundamental changes associated with the malignant transformation ofplasma cells.

[0057] The progression of multiple myeloma as a hypoproliferative tumoris thought to be linked to a defect in programmed cell death rather thanrapid cell replication. Two genes, prohibitin (PHB) and quiescin Q6(QSCN6), overexpressed in multiple myeloma are involved in growtharrest. The overexpression of these genes may be responsible for thetypically low proliferation indices seen in multiple myeloma. It ishence conceivable that therapeutic downregulation of these genes thatresults in enhanced proliferation could render multiple myeloma cellsmore susceptible to cell cycle-active chemotherapeutic agents.

[0058] The gene coding for CD27, TNFRSF7, the second most significantlyunderexpressed gene in multiple myeloma, is a member of the tumornecrosis factor receptor (TNFR) superfamily that provides co-stimulatorysignals for T and B cell proliferation and B cell immunoglobulinproduction and apoptosis. Anti-CD27 significantly inhibits the inductionof Blimp-1 and J-chain transcripts which are turned on in cellscommitted to plasma cell differentiation, suggesting that ligation ofCD27 on B cells may prevent terminal differentiation. CD27 ligand (CD70)prevents IL-10-mediated apoptosis and directs differentiation of CD27⁺memory B cells toward plasma cells in cooperation with IL-10. Thus, itis possible that the downregulation of CD27 gene expression in multiplemyeloma may block an apoptotic program.

[0059] The overexpression of CD47 on multiple myeloma may be related toescape of multiple myeloma cells from immune surveillance. Studies haveshown that cells lacking CD47 are rapidly cleared from the bloodstreamby splenic red pulp macrophages and CD47 on normal red blood cellsprevents this elimination.

[0060] The gene product of DNA methyltransferase 1, DNMT1, overexpressedin multiple myeloma, is responsible for cytosine methylation in mammalsand has an important role in epigenetic gene silencing. In fact,aberrant hypermethylation of tumor suppressor genes plays an importantrole in the development of many tumors. De novo methylation of p16/INK4ais a frequent finding in primary multiple myeloma. Also, recent studieshave shown that upregulated expression of DNMTs may contribute to thepathogenesis of leukemia by inducing aberrant regional hypermethylation.DNA methylation represses genes partly by recruitment of themethyl-CpG-binding protein MeCP2, which in turn recruits a histonedeacetylase activity. It has been shown that the process of DNAmethylation, mediated by Dnmt1, may depend on or generate an alteredchromatin state via histone deacetylase activity.

[0061] It is potentially significant that multiple myeloma cases alsodemonstrate significant overexpression of the gene formetastasis-associated 1 (MTA1). MTA1 was originally identified as beinghighly expressed in metastatic cells. MTA1 has more recently beendiscovered to be one subunit of the NURD (NUcleosome Remodeling andhistone Deacetylation) complex which contains not only ATP-dependentnucleosome disruption activity, but also histone deacetylase activity.Thus, over expression of DNMT1 and MTA1 may have dramatic effects onrepressing gene expression in multiple myeloma.

[0062] Oncogenes activated in multiple myeloma included ABL and MYC.Although it is not clear whether ABL tyrosine kinase activity is presentin multiple myeloma, it is important to note that overexpression of abland c-myc results in the accelerated development of mouse plasmacytomas.Thus, it may be more than a coincidence that multiple myeloma cellssignificantly overexpresses MYC and ABL.

[0063] Chromosomal translocations involving the MYC oncogene and IGH andIGL genes that result in dysregulated MYC expression are hallmarks ofBurkitt's lymphoma and experimentally induced mouse plasmacytomas;however, MYC/IGH-associated translocations are rare in multiple myeloma.Although high MYC expression was a common feature in our panel ofmultiple myeloma, it was quite variable, ranging from little or noexpression to highly elevated expression. It is also of note that theMAZ gene whose product is known to bind to and activate MYC expressionwas significantly upregulated in the MM4 subgroup. Given the importantrole of MYC in B cell neoplasia, it is speculated that overexpression ofMYC, and possibly ABL, in multiple myeloma may have biological andpossibly prognostic significance.

[0064] EXT1 and EXT2, which are tumor suppressor genes involved inhereditary multiple exostoses, heterodimerize and are critical in thesynthesis and display of cell surface heparan sulfate glycosaminoglycans(GAGs). EXT1 is expressed in both multiple myeloma and normal plasmacells. EXT2L was overexpressed in multiple myeloma, suggesting that afunctional glycosyltransferase could be created in multiple myeloma. Itis of note that syndecan-1 (CD138/SDC1), a transmembrane heparan sulfateproteoglycan, is abundantly expressed on multiple myeloma cells and,when shed into the serum, is a negative prognostic factor. Thus,abnormal GAG-modified SDC1 may be important in multiple myeloma biology.The link of SDC1 to multiple myeloma biology is further confirmed by therecent association of SDC1 in the signaling cascade induced by the WNTproto-oncogene products. It has been showed that syndecan-1 (SDC1) isrequired for Wnt-1-induced mammary tumorigenesis. Data disclosed hereinindicated a significant downregulation of WNT10B in primary multiplemyeloma cases. It is also of note that the WNT5A gene and the FRZB gene,which codes for a decoy WNT receptor, were also marginally upregulatedin newly diagnosed multiple myeloma. Given that the WNTs represent anovel class of B cell regulators, deregulation of the expression ofthese growth factors (WNT5A, WNT10B) and their receptors (e.g., FRZB)and genes products that modulate receptor signaling (e.g., SDC1), may beimportant in the genesis of multiple myeloma.

[0065] The present invention also identifies, through multivariatestepwise discriminant analysis, a minimum subset of genes whoseexpression is intimately associated with the malignant features ofmultiple myeloma. By applying linear regression analysis to the top 50statistically significant differentially expressed genes, 14 genes weredefined as predictors that are able to differentiate multiple myelomafrom normal plasma cells with a high degree of accuracy. When the modelwas applied to a validation group consisting of 118 multiple myeloma, 6normal plasma cells and 7 cases of MGUS, an accuracy of classificationof more than 99% was achieved. Importantly, 6 of the 7 MGUS cases wereclassified as multiple myeloma, indicating that MGUS has gene expressionfeatures of malignancy. Thus the altered expression of 14 genes out ofover 6,000 genes interrogated are capable of defining multiple myeloma.Similar multivariate discriminant analysis also identified a set of 24genes that can distinguish between the four multiple myeloma subgroupsdescribed above.

[0066] In addition to identifying genes that were statisticallydifferent between the group of normal plasma cells and multiple myelomaplasma cells, the present invention also identified genes, like FGFR3and CCND1, that demonstrate highly elevated “spiked” expression insubsets of multiple myelomas. Patients with elevated expression of thesegenes can have significant distribution differences among the four geneexpression cluster subgroups. For example, FGFR3 spikes are found in MM1and MM2 whereas spikes of IFI27 are more likely to be found in MM3 andMM4. Highly elevated expression of the interferon-induced gene IFI27 maybe indicative of a viral infection, either systemic or specificallywithin the plasma cells from these patients. Correlation analysis hasshown that IFI27 spikes are significantly linked (Pearson correlationcoefficient values of 0.77 to 0.60) to elevated expression of 14interferon-induced genes, including MX1, MX2, OAS1, OAS2, IFIT1, IFIT4,PLSCR1, and STAT1. More recent analysis of a large population ofmultiple myeloma patients (N=280) indicated that nearly 25% of allpatients had spikes of the IFI27 gene. It is of interest to determinewhether or not the IFI27 spike patients who cluster in the MM4 subgroupare more likely to have a poor clinical course and to identify thesuspected viral infection causing the upregulation of this class ofgenes. Thus, spiked gene expression may also be used in the developmentof clinically relevant prognostic groups.

[0067] Finally, the 100% coincidence of spiked FGFR3 or CCND1 geneexpression with the presence of the t(4;14)(p14;q32) ort(11;14)(q13;q32) translocations, as well as the strong correlationsbetween protein expression and gene expression represent importantvalidations of the accuracy of gene expression profiling and suggeststhat gene expression profiling may eventually supplant the laborintensive and expensive clinical laboratory procedures, such as cellsurface marker immunophenotyping and molecular and cellularcytogenetics.

[0068] Genes identified by the present invention that showssignificantly up-regulated or down-regulated expression in multiplemyeloma are potential therapeutic targets for multiple myeloma.Over-expressed genes may be targets for small molecules or inhibitorsthat decrease their expression. Methods and materials that can be usedto inhibit gene expression, e.g. small drug molecules, anti-sense oligo,or antibody would be readily apparent to a person having ordinary skillin this art. On the other hand, under-expressed genes can be replaced bygene therapy or induced by drugs.

[0069] Comparison of Multiple Myeloma with Normal Plasma CellDevelopment

[0070] Data disclosed herein indicated that multiple myeloma can beplaced into a developmental schema parallel to that of normal plasmacell differentiation. Global gene expression profiling reveals distinctchanges in transcription associated with human plasma celldifferentiation. Hierarchical clustering analyses with 4866 genessegregated tonsil B cells, tonsil plasma cells, and bone marrow plasmacells. Combining χ² and Wilcoxon rank sum tests, 359 previously definedand novel genes significantly (P<0.0005) discriminated tonsil B cellsfrom tonsil plasma cells, and 500 genes significantly discriminatedtonsil plasma cells from bone marrow plasma cells. Genes that weresignificantly differentially expressed in the tonsil B cell to tonsilplasma cell transition were referred as “early differentiation genes”(EDGs) and those differentially expressed in the tonsil plasma cell tobone marrow plasma cell transition were referred as “latedifferentiation genes” (LDGs). One-way ANOVA was then applied to EDGsand LDGs to identify statistically significant expression differencesbetween multiple myeloma (MM) and tonsil B cells (EDG-MM), tonsil plasmacells (LDG-MM1), or bone marrow plasma cells (LDG-MM2).

[0071] Hierarchical cluster analysis revealed that 13/18 (P=0.00005) MM4cases (a putative poor-prognosis subtype) clustered tightly with tonsilB cells. The other groups (MM1, 2 and 3) failed to show suchassociations. In contrast, there was tight clustering between tonsilplasma cells and 14/15 (P=0.00001) MM3, and significant similaritiesbetween bone marrow plasma cells and 14/20 (P=0.00009) MM2 cases werefound. MM1 showed no significant linkage with the normal cell typesstudied. In addition, XBP1, a transcription factor essential for plasmacell differentiation, exhibited a significant, progressive reduction inexpression from MM1 to MM4, consistent with developmental-stagerelationships. Therefore, global gene expression patterns linked tolate-stage B cell differentiation confirmed and extended a global geneexpression-defined classification system of multiple myeloma, suggestingthat multiple myeloma represents a constellation of distinct subtypes ofdisease with unique origins.

[0072] In summary, the present invention is drawn to a method of geneexpression profiling for multiple myeloma. Nucleic acid samples ofisolated plasma cells derived from individuals with or without multiplemyeloma were applied to a DNA microarray, and hierarchical clusteringanalysis performed on data obtained from the microarray will classifythe individuals into distinct subgroups such as the MM1, MM2, MM3 andMM4 subgroups disclosed herein. The profiling will also identify geneswith elevated expression in subsets of multiple myeloma patients orgenes with significantly different levels of expression in multiplemyeloma patients as compared to normal individuals. These genes arepotential therapeutic targets for multiple myeloma. Representativeexamples of these genes are listed in Tables 4, 5 and 8.

[0073] In another embodiment of the present invention, there is provideda method of identifying a group of genes that can distinguish betweennormal plasma cells and plasma cells of multiple myeloma. Nucleic acidsamples of isolated plasma cells derived from individuals with orwithout multiple myeloma were applied to a DNA microarray, andhierarchical clustering analysis was performed on data obtained from themicroarray. Genes with statistically significant differential expressionpatterns were identified, and linear regression analysis was used toidentify a group of genes that is capable of accurate discriminationbetween normal plasma cells and plasma cells of multiple myeloma.Representative examples of these genes are listed in Table 6. Thisanalysis can also identify a group of genes that is capable of accuratediscrimination between subgroups of multiple myeloma. Representativeexamples of these genes are listed in Table 7.

[0074] Expression levels of such a group of 14 genes as listed in Table6 or a group of 24 genes as listed in Table 7 could be used fordiagnosis of multiple myeloma. Significant differential expression ofthese genes would indicate that such individual has multiple myeloma ora subgroup of multiple myeloma. Gene expression levels can be examinedat nucleic acid level or protein level according to methods well knownto one of skill in the art.

[0075] In another embodiment of the present invention, there areprovided methods of treatment for multiple myeloma. Such methods involveinhibiting expression of one of the genes listed in Table 5 or Table 8,or increasing expression of one of the genes listed in Table 4. Methodsof inhibiting or increasing gene expression such as those usinganti-sense oligonucleotide or antibody are well known to one of skill inthe art. Inhibiting gene expression can be achieved through RNAinterference using so called siRNA. Gene expression enhancement might bethrough gene therapy.

[0076] The present invention is also drawn to a method of developmentalstage-based classification for multiple myeloma. Nucleic acid samples ofisolated B cells and plasma cells derived from individuals with orwithout multiple myeloma were applied to a DNA microarray, andhierarchical clustering analysis performed on data obtained from themicroarray will classify the multiple myeloma cells according to thedevelopmental stages of normal B cells and plasma cells. In general,normal B cells and plasma cells are isolated from tonsil, bone marrow,mucoal tissue, lymph node or peripheral blood.

[0077] The present invention also provides a method of controlling boneloss in an individual by inhibiting the expression of the DKK1 gene(accession number NM012242). In general, DKK1 expression can beinhibited by anti-sense oligonucleotides or anti-DKK1 antibodies. Inanother embodiment, bone loss can be controlled by a pharmacologicalinhibitor of DKK1 protein. Preferably, the individual having bone lossmay have multiple myeloma, osteoporosis, post-menopausal osteoporosis ormalignancy-related bone loss that is caused by breast cancer metastasisor prostate cancer metastasis.

[0078] The following examples are given for the purpose of illustratingvarious embodiments of the invention and are not meant to limit thepresent invention in any fashion. One skilled in the art will appreciatereadily that the present invention is well adapted to carry out theobjects and obtain the ends and advantages mentioned, as well as thoseobjects, ends and advantages inherent herein. Changes therein and otheruses which are encompassed within the spirit of the invention as definedby the scope of the claims will occur to those skilled in the art.

EXAMPLE 1

[0079] Cell Isolation and Analysis

[0080] Samples for the following studies included plasma cells from 74newly diagnosed cases of multiple myeloma, 5 subjects with monoclonalgammopathy of undetermined significance, 7 samples of tonsil Blymphocytes (tonsil BCs), 11 samples of tonsil plasma cells (tonsilPCs), and 31 bone marrow PCs derived from normal healthy donor. Multiplemyeloma cell lines (U266, ARP1, RPMI-8226, UUN, ANBL-6, CAG, and H929(courtesy of P. L. Bergsagel) and an Epstein-Barr virus(EBV)-transformed B-lymphoblastoid cell line (ARH-77) were grown asrecommended (ATCC, Chantilly, Va.).

[0081] Tonsils were obtained from patients undergoing tonsillectomy forchronic tonsillitis. Tonsil tissues were minced, softly teased andfiltered. The mononuclear cell fraction from tonsil preparations andbone marrow aspirates were separated by a standard FicolI-Hypaquegradient (Pharmacia Biotech, Piscataway, N.J.). The cells in the lightdensity fraction (S.G.≦1.077) were resuspended in cell culture media and10% fetal bovine serum, RBC lysed, and several PBS wash steps wereperformed. Plasma cell isolation was performed with anti-CD138immunomagnetic bead selection as previously described (Zhan et al.,2002a). B lymphocyte isolation was performed using directly conjugatedmonoclonal mouse anti-human CD19 antibodies and the AutoMacs automatedcell sorter (Miltenyi-Biotec, Auburn, Calif.).

[0082] For cytology, approximately 40,000 purified tonsil BC and PCmononuclear cells were cytocentrifuged at 1000×g for 5 min at roomtemperature. For morphological studies, the cells were immediatelyprocessed by fixing and staining with DiffQuick fixative and stain (DadeDiagnostics, Aguada, PR).

[0083] For immunofluorescence, slides were treated essentially asdescribed (Shaughnessy et al., 2000). Briefly, slides were air-driedovernight, then fixed in 100% ethanol for 5 min at room temperature andbaked in a dry 37° C. incubator for 6 hr. The slides were then stainedwith 100 μl of a 1:20 dilution of goat anti-human-kappa immunoglobulinlight chain conjugated with 7-amino-4-methylcourmarin-3-acitic acid(AMCA) (Vector Laboratories, Burlingame, Calif.) for 30 min in ahumidified chamber. After incubation, the slides were washed two timesin 1×PBS+0.1% NP-40 (PBD). To enhance the AMCA signal, the slides wereincubated with 100 μl of a 1:40 dilution of AMCA-labeledrabbit-anti-goat IgG antibody and incubated for 30 min at roomtemperature in a humidified chamber. Slides were washed 2 times in1×PBD. The slides were then stained with 100 μl of a 1:100 dilution ofgoat anti-human-lambda immunoglobulin light chain conjugated with FITC(Vector Laboratories, Burlingame, Calif.) for 30 min in a humidifiedchamber; the slides were washed two times in 1×PBD. Then the slides werestained with propidium iodide at 0.1 μg/ml in 1×PBS for 5 min, washed in1×PBD, and 10 μl anti-fade (Molecular Probes, Eugene, Oreg.) was addedand coverslips were placed. Cytoplasmic immunoglobulin lightchain-positive PCs were visualized using an Olympus BX60epi-fluorescence microscope equipped with appropriate filters. Theimages were captured using a Quips XL genetic workstation (Vysis,Downers Grove, Ill.)

[0084] Both unpurified mononuclear cells and purified fractions fromtonsil BCs, tonsil PCs, and bone marrow PCs were subjected to flowcytometric analysis of CD marker expression using a panel of antibodiesdirectly conjugated to FITC or PE. Markers used in the analysis includedFITC-labeled CD20, PE-labeled CD38, FITC-labeled or ECD-labeled CD45,PE- or PC5-labled CD138 (Beckman Coulter, Miami, Fla.). For detection ofCD138 on PCs after CD138 selection, we employed an indirect detectionstrategy using a FITC-labeled rabbit anti-mouse IgG antibody (BeckmanCoulter) to detect the mouse monoclonal anti-CD138 antibody BB4 used inthe immunomagnetic selection technique. Cells were taken after FicolIHypaque gradient or after cell purification, washed in PBS, and stainedat 4° C. with CD antibodies or isotype-matched control G1 antibodies(Beckman Coulter). After staining, cells were resuspended in 1×PBS andanalyzed using a Epics XL-MCL flow cytometry system (Beckman Coulter).

EXAMPLE 2

[0085] Preparation of Labeled cRNA and Hybridization to High-DensityMicroarray

[0086] Total RNA was isolated with RNeasy Mini Kit (Qiagen, Valencia,Calif.). Double-stranded cDNA and biotinylated cRNA were synthesizedfrom total RNA and hybridized to HuGeneFL GeneChip microarrays(Affymetrix, Santa Clara, Calif.), which were washed and scannedaccording to procedures developed by the manufacturer. The arrays werescanned using Hewlett Packard confocal laser scanner and visualizedusing Affymetrix 3.3 software (Affymetrix). Arrays were scaled to anaverage intensity of 1,500 and analyzed independently.

EXAMPLE 3

[0087] Genechip Data Analysis

[0088] To efficiently manage and mine high-density oligonucleotide DNAmicroarray data, a new data-handling tool was developed.GeneChip-derived expression data was stored on an MS SQL Server. Thisdatabase was linked, via an MS Access interface called ClinicalGene-Organizer to multiple clinical parameter databases for multiplemyeloma patients. This Data Mart concept allows gene expression profilesto be directly correlated with clinical parameters and clinical outcomesusing standard statistical software. All data used in the presentanalysis were derived from Affymetrix 3.3 software. GeneChip 3.3 outputfiles are given (1) as an average difference (AD) that represents thedifference between the intensities of the sequence-specific perfectmatch probe set and the mismatch probe set, or (2) as an absolute call(AC) of present or absent as determined by the GeneChip 3.3 algorithm.Average difference calls were transformed by the natural log aftersubstituting any sample with an average difference of <60 with the value60 (2.5 times the average Raw Q). Statistical analysis of the data wasperformed with software packages SPSS 10.0 (SPSS, Chicago, Ill.), S-Plus2000 (Insightful Corp., Seattle, Wash.), and Gene Cluster/Treeview(Eisen et al., 1998).

[0089] To differentiate four distinct subgroups of multiple myeloma(MM1, MM2, MM3 and MM4), hierarchical clustering of average linkageclustering with the centered correlation metric was employed. Theclustering was done on the average difference data of 5,483 genes.Either Chi square (χ²) or Fisher's exact test was used to findsignificant differences between cluster groups with the AC data. Tocompare the expression levels, the non-parametric Wilcoxon rank sum(WRS) test was used. This test uses a null hypothesis that is based onranks rather than on normally distributed data. Before the above testswere performed, genes that were absent (AC) across all samples wereremoved; 5,483 genes were used in the analyses. Genes that weresignificant (P<0.0001) for both the χ² test and the WRS test wereconsidered to be significantly differentially expressed.

[0090] Clinical parameters were tested across multiple myeloma clustergroups. ANOVA test was used to test the continuous variables, and χ²test of independence or Fisher's exact test was applied to test discretevariables. The natural log of the average difference data was used tofind genes with a “spiked profile” of expression in multiple myeloma.Genes were identified that had low to undetectable expression in themajority of patients and normal samples (no more than 4 present absolutecalls [P-AC]). A total of 2,030 genes fit the criteria of this analysis.The median expression value of each of the genes across all patientsamples was determined. For the i^(th) gene, this value was calledmedgene (i). The i^(th) gene was a “spiked” gene if it had at least 4patient expression values >2.5+medgene (i). The constant 2.5 was basedon the log of the average difference data. These genes that were“spiked” were further divided into subsets according to whether or notthe largest spike had an average difference expression value greaterthan 10,000.

[0091] To determine transcriptional changes associated with human plasmacell differentiation, a total of 4866 genes were scanned across 7 caseseach of tonsil B cells, tonsil plasma cells, and bone marrow plasmacells. The 4866 genes were derived from 6800 by filtering out allcontrol genes, and genes not fulfilling the test of Max−Min<1.5 (1.5being the natural log of the average difference). The χ² test was usedto eliminate genes with absent absolute call (AAC). For example, in thetonsil plasma cell to bone marrow plasma cell comparison, genes with χ²values greater than 3.84 (P<0.05) or having “present” AC (PAC) in morethan half of the samples in each group were retained. In the tonsil Bcell to tonsil plasma cell and tonsil plasma cell to bone marrow plasmacell comparisons, 2662 and 2549 genes were retained as discriminatingbetween the two groups, respectively. To compare gene expression levels,the non-parametric Wilcoxon Rank Sum (WRS) test was used to compare twogroups using natural log transformed AD. The cutoff P value depended onthe sample size, the heterogeneity of the two comparative populations(tonsil B cells, tonsil plasma cells, and bone marrow plasma cellsshowed a higher degree of stability in AD), and the degree ofsignificance. In this analysis, 496 and 646 genes were found to besignificantly (P<0.0005) differentially expressed in the tonsil B cellversus tonsil plasma cell and tonsil plasma cell versus bone marrowplasma cell comparisons, respectively. To define the direction ofsignificance (expression changes being up or down in one group comparedwith the other), the non-parametric Spearman correlation test of the ADwas employed.

[0092] Genes that were significantly differentially expressed in thetonsil B cell to tonsil plasma cell transition were referred as “earlydifferentiation genes” (EDGs) and those differentially expressed in thetonsil plasma cell to bone marrow plasma cell transition were referredas “late differentiation genes” (LDGs). Previously defined and novelgenes were identified that significantly discriminated tonsil B cellsfrom tonsil plasma cells (359 genes) and tonsil plasma cells from bonemarrow plasma cells (500 genes).

[0093] To classify multiple myeloma with respect to EDG and LDG, 74newly diagnosed cases of multiple myeloma and 7 tonsil B cell, 7 tonsilplasma cell, and 7 bone marrow plasma cell samples were tested forvariance across the 359 EDGs and 500 LDGs. The top 50 EDGs that showedthe most significant variance across all samples were defined as earlydifferentiation genes for myeloma (EDG-MM). Likewise, the top 50 LDGsshowing the most significant variance across all samples were identifiedas late differentiation genes for myeloma-1 (LDG-MM1). Subtracting theLDG-MM1 from the 500 LDG and then applying one-way ANOVA test forvariance to the remaining genes identified the top 50 genes showingsimilarities between bone marrow plasma cells and multiple myeloma.These genes were defined as LDG-MM2.

[0094] Hierarchical clustering was applied to all samples using 30 ofthe 50 EDG-MM. A total of 20 genes were filtered out with Max−Min<2.5.This filtering was performed on this group because many of the top 50EDG-MM showed no variability across multiple myeloma and thus could notbe used to distinguish multiple myeloma subgroups. A similar clusteringstrategy was employed to cluster the samples using the 50 LDG-MM1 and 50LDG-MM2; however, in these cases all 50 significant genes were used inthe cluster analysis.

EXAMPLE 4

[0095] RT-PCR and Immunohistochemistry

[0096] RT-PCR for the FGFR3 MMSET was performed on the same cDNAs usedin the microarray analysis. Briefly, cDNA was mixed with the IGJH2(5′-CAATGGTCACCGTCTCTTCA-3′, SEQ ID No. 1) primer and the MMSET primer(5′-CCTCAATTTCCTGAAATTGGTT-3′, SEQ ID No. 2). PCR reactions consisted of30 cycles with a 58° C. annealing temperature and 1-minute extensiontime at 72° C. using a Perkin-Elmer GeneAmp 2400 thermocycler(Wellesley, Mass.). PCR products were visualized by ethidium bromidestaining after agarose gel electrophoresis.

[0097] Immunohistochemical staining was performed on a Ventana ES(Ventana Medical Systems, Tucson, Ariz.) using Zenker-fixedparaffin-embedded bone marrow sections, an avidin-biotin peroxidasecomplex technique (Ventana Medical Systems), and the antibody L26 (CD20,Ventana Medical Systems). Heat-induced epitope retrieval was performedby microwaving the sections for 28 minutes in a 1.0-mmol/L concentrationof citrate buffer at pH 6.0.

EXAMPLE 5

[0098] Interphase FISH

[0099] For interphase detection of the t(11;14)(q13;q32) translocationfusion signal, a LSI IGH/CCND1 dual-color, dual-fusion translocationprobe was used (Vysis, Inc, Downers Grove, Ill.). The TRI-FISH procedureused to analyze the samples has been previously described. Briefly, atleast 100 clonotypic plasma cells identified by cIg staining werecounted for the presence or absence of the translocation fusion signalin all samples except one, which yielded only 35 plasma cells. Anmultiple myeloma sample was defined as having the translocationwhen >25% of the cells contained the fusion.

EXAMPLE 6

[0100] Hierarchical Clustering of Plasma Cell Gene ExpressionDemonstrates Class Distinction

[0101] As a result of 656,000 measurements of gene expression in 118plasma cell samples, altered gene expression in the multiple myelomasamples was identified. Two-dimensional hierarchical clusteringdifferentiated cell types by gene expression when performed on 5,483genes that were expressed in at least one of the 118 samples (FIG. 1A).The sample dendrogram derived two major branches (FIGS. 1A and 1D). Onebranch contained all 31 normal samples and a single monoclonalgammopathy of undetermined significance case whereas the second branchcontained all 74 multiple myeloma and 4 monoclonal gammopathy ofundetermined significance cases and the 8 cell lines. The multiplemyeloma-containing branch was further divided into two sub-branches, onecontaining the 4 monoclonal gammopathy of undetermined significance andthe other the 8 multiple myeloma cell lines. The cell lines were allclustered next to one another, thus showing a high degree of similarityin gene expression among the cell lines. This suggested that multiplemyeloma could be differentiated from normal plasma cells and that atleast two different classes of multiple myeloma could be identified, onemore similar to monoclonal gammopathy of undetermined significance andthe other similar to multiple myeloma cell lines.

[0102] Hierarchical clustering analysis with all 118 samples togetherwith duplicate samples from 12 patients (plasma cells taken 24 hr or 48hr after initial sample) were repeated to show reproducibility of thetechnique and analysis. All samples from the 12 patients studiedlongitudinally were found to cluster adjacent to one another. Thisindicated that gene expression in samples from the same patient weremore similar to each other than they were to all other samples (data notshown).

[0103] In addition to the demonstration of reproducibility of clusteringnoted above, three microarray analyses were also performed on a singlesource of RNA from one patient. When included in the cluster analysis,the three samples clustered adjacent to one another. Consistent with themanufacturer's specification, an analysis of the fold changes seen inthe samples showed that <2% of all genes had a >2-fold difference.Hence, these data indicated reproducibility for same samples.

[0104] The clustergram (FIG. 1A) showed that genes of unrelated sequencebut similar function clustered tightly together along the vertical axis.For example, a particular cluster of 22 genes, primarily those encodingimmunoglobulin molecules and major histocompatibility genes, hadrelatively low expression in multiple myeloma plasma cells and highexpression in normal plasma cells (FIG. 1B). This was anticipated, giventhat the plasma cells isolated from multiple myeloma are clonal andhence only express single immunoglobulin light-chain and heavy-chainvariable and constant region genes, whereas plasma cells from normaldonors are polyclonal and express many different genes of these twoclasses. Another cluster of 195 genes was highly enriched for numerousoncogenes/growth-related related genes (e.g., MYC, ABL1, PHB, and EXT2),cell cycle-related genes (e.g., CDC37, CDK4, and CKS2), and translationmachinery genes (EIF2, EIF3, HTF4A, and TFIIA) (FIG. 1C). These geneswere all highly expressed in MM, especially in multiple myeloma celllines, but had low expression levels in normal plasma cells.

EXAMPLE 7

[0105] Hierarchical Clustering of Newly Diagnosed Multiple MyelomaIdentifies Four Distinct Subgroups

[0106] Two-dimensional cluster analysis was performed on the 74 multiplemyeloma cases alone. The sample dendrogram identified two major brancheswith two distinct subgroups within each branch (FIG. 1E). The foursubgroups were designated MM1, MM2, MM3, and MM4 containing 20, 21, 15,and 18 patients respectively. The MM1 subgroup represented the patientswhose plasma cells were most closely related to the monoclonalgammopathy of undetermined significance plasma cells and MM4 were mostlike the multiple myeloma cell lines (see FIG. 1D). These data suggestedthat the four gene expression subgroups were authentic and mightrepresent four distinct clinical entities.

[0107] Differences in gene expression across the four subgroups werethen examined using the χ² and WRS tests (Table 1). As expected thelargest difference was between MM1 and MM4 (205 genes) and the smallestdifference was between MM1 and MM2 (24 genes). Next, the top 30 genesturned on or upregulated in MM4 as compared with MM1 were examined(Table 2). The data demonstrated that 13 of 30 most significant genes(10 of the top 15 genes) were involved in DNA replication/repair or cellcycle control. Thymidylate synthase (TYMS), which was present in all 18samples comprising the MM4 subgroup, was only present in 3 of the 20 MM1samples and represented the most significant gene in the χ² test. TheDNA mismatch repair gene, mutS (E. coli) homolog 2 (MSH2) with a WRS Pvalue of 2.8×10⁻⁶ was the most significant gene in the WRS test. Othernotable genes in the list included the CAAX farnesyltransferase (FNTA),the transcription factors enhancer of zeste homolog 2 (EZH2) andMYC-associated zinc finger protein (MAZ), eukaryotic translationinitiation factors (EIF2S1 and EIF2B1), as well as the mitochondrialtranslation initiation factor 2 (MTIF2), the chaperone (CCT4), theUDP-glucose pyrophosphorylase 2 (IUGP2), and the 26Sproteasome-associated padl homolog (POH1).

[0108] To assess the validity of the clusters with respect to clinicalfeatures, correlations of various clinical parameters across the 4subgroups were analyzed (Table 3). Of 17 clinical variables tested, thepresence of an abnormal karyotype (P=0.0003) and serum β2M levels(P=0.0005) were significantly different among the four subgroups andincreased creatinine (P=0.06) and cytogenetic deletion of chromosome 13(P=0.09) were marginally significant. The trend was to have higher β2Mand creatinine as well as an abnormal karyotype and chromosome 13deletion in the MM4 subgroup as compared with the other 3 subgroups.TABLE 1 Differences In Gene Expression Among Multiple Myeloma SubgroupsComparison Range of WRS* P Values Number of Genes MM1 vs MM4 .00097 to9.58 × 10⁻⁷ 205 MM2 vs MM4 .00095 to 1.0410⁻⁶ 162 MM3 vs MM4 .00098 to3.7510⁻⁶ 119 MM1 vs MM3 .00091 to 6.2710⁻⁶  68 MM2 vs MM3 .00097 to1.9810⁻⁵  44 MM1 vs MM2 .00083 to 2.9310⁻⁵  24

[0109] TABLE 2 The 30 Most Differentially Expressed Genes In AComparison Of MM1 And MM4 Subgroups MM1 MM4 Gene (N = 20) (N = 18) ChiWRS^(‡) Accession* Function Symbol P P Square P Value D00596 DNAreplication TYMS  3 18 24.35 1.26 × 10⁻⁴ U35835 DNA repair PRKDC  2 1723.75 4.55 × 10⁻⁶ U77949 DNA replication CDC6  1 13 15.62 5.14 × 10⁻⁶U91985 DNA fragmentation DFFA  1 12 13.38 6.26 × 10⁻⁵ U61145transcription EZH2  4 15 12.77 1.67 × 10⁻⁴ U20979 DNA replication CHAF1A 2 12 10.75 1.10 × 10⁻⁴ U03911 DNA repair MSH2  0  9 10.48 2.88 × 10⁻⁶X74330 DNA replication PRIM1  0  9 10.48 9.36 × 10⁻⁶ X12517 SnRNP SNR PC 0  9 10.48 5.26 × 10⁻⁶ D85131 transcription MAZ  0  9 10.48 1.08 × 10⁻⁵L00634 farnesyltransferase FNTA 10 18 9.77 7.28 × 10⁻⁵ U21090 DNAreplication POLD2 11 18 8.27 8.05 × 10⁻⁵ X54941 cell cycle CKS1 10 177.07 1.26 × 10⁻⁴ U62136 cell cycle UBE2V2 13 18 5.57 4.96 × 10⁻⁶ D38076cell cycle RANBP1 13 18 5.57 7.34 × 10⁻⁶ X95592 unknown C1D^(†) 13 185.57 1.10 × 10⁻⁴ X66899 cell cycle EWSR1 14 18 4.35 1.89 × 10⁻⁴ L34600translation initiation MTIF2 14 18 4.35 3.09 × 10⁻⁵ U27460 MetabolismIUGP2 15 18 3.22 1.65 × 10⁻⁴ U15009 SnRNP SNRPD3 15 18 3.22 1.47 × 10⁻⁵J02645 translation initiation EIF2S1 16 18 2.18 7.29 × 10⁻⁵ X95648translation initiation EIF2B1 16 18 2.18 1.45 × 10⁻⁴ M34539 calciumsignaling FKBP1A 18 18 0.42 1.71 × 10⁻⁵ J04611 DNA repair G22P1 18 180.42 7.29 × 10⁻⁵ U67122 anti-apoptosis UBL1 20 18 0.00 7.29 × 10⁻⁵U38846 chaperon CCT4 20 18 0.00 1.26 × 10⁻⁴ U80040 metabolism ACO2 20 180.00 8.38 × 10⁻⁵ U86782 proteasome POH^(†) 20 18 0.00 5.90 × 10⁻⁵ X57152signaling CSNK2B 20 18 0.00 7.29 × 10⁻⁵ D87446 unknown KIAA0257^(†) 2018 0.00 1.26 × 10⁻⁵

[0110] TABLE 3 Clinical Parameters Linked To Multiple Myeloma SubgroupsMultiple Myeloma Subgroups Clinical Parameter 1 2 3 4 P value Abnormalcytogenetics 40.0% 5.3% 53.3% 72.2% .00028 Average β2-microglobulin 2.812.73 4.62 8.81 .00047 (mg/L)

EXAMPLE 8

[0111] Altered Expression of 120 Genes in Malignant Plasma Cells

[0112] Hierarchical cluster analysis disclosed above showed thatmultiple myeloma plasma cells could be differentiated from normal plasmacells. Genes distinguishing the multiple myeloma from normal plasmacells were identified as significant by χ² analysis and the WRS test(P<0.0001). A statistical analysis showed that 120 genes distinguishedmultiple myeloma from normal plasma cells. Pearson correlation analysesof the 120 differentially expressed genes were used to identify whetherthe genes were upregulated or downregulated in MM.

[0113] When genes associated with immune function (e.g. IGH, IGL, HLA)that represent the majority of significantly downregulated genes werefiltered out, 50 genes showed significant downregulation in multiplemyeloma (Table 4). The P values for the WRS test ranged from 9.80×10⁻⁵to 1.56×10⁻¹⁴, and the χ² test of the absence or presence of theexpression of the gene in the groups ranged from 18.83 to 48.45. Thegene representing the most significant difference in the χ² test was theCXC chemokine SDF1. It is important to note that a comparison ofmultiple myeloma plasma cells to tonsil-derived plasma cells showedthat, like multiple myeloma plasma cells, tonsil plasma cells also donot express SDF1. Two additional CXC chemokines, PF4 and PF4V1, werealso absent in multiple myeloma plasma cells. The second mostsignificant gene was the tumor necrosis factor receptor super familymember TNFRF7 coding for CD27, a molecule that has been linked tocontrolling maturation and apoptosis of plasma cells.

[0114] The largest group of genes, 20 of 50, were linked to signalingcascades. multiple myeloma plasma cells have reduced or no expression ofgenes associated with calcium signaling (S100A9 and S100A12) orlipoprotein signaling (LIPA, LCN2, PLA2G7, APOE, APOC1). LCN2, alsoknown as 24p3, codes for secreted lipocalin, which has recently beenshown to induce apoptosis in pro B-cells after growth factordeprivation. Another major class absent in multiple myeloma plasma cellswas adhesion-associated genes (ITGA2B, IGTB2, GP5, VCAM, and MIC2).

[0115] Correlation analysis showed that 70 genes were either turned onor upregulated in multiple myeloma (Table 5). When considering the χ²test of whether expression is present or absent, the cyclin-dependentinhibitor, CDKN1A, was the most significantly differentially expressedgene (χ²=53.33, WRS=3.65×10⁻¹¹). When considering a quantitative changeusing the WRS test, the tyrosine kinase oncogene ABL1 was the mostsignificant (χ²=43.10, WRS=3.96×10⁻¹⁴). Other oncogenes in the listincluded USF2, USP4, MLLT3 and MYC. The largest class of genesrepresented those whose products are involved in protein metabolism (12genes), including amino acid synthesis, translation initiation, proteinfolding, glycosylation, trafficking, and protein degradation. Othermultiple-member classes included transcription (11 genes), signaling (9genes), DNA synthesis and modification (6 genes), and histone synthesisand modification (5 genes).

[0116] Overexpression of signaling genes such as QSCN6, PHB,phosphatases PTPRK and PPP2R4, and the kinase MAPKAPK3 has been linkedto growth arrest. The only secreted growth factor in the signaling classwas HGF, a factor known to play a role in multiple myeloma biology. TheMOX2 gene, whose product is normally expressed as an integral membraneprotein on activated T cells and CD19⁺ B cells and involved ininhibiting macrophage activation, was in the signaling class. The tumorsuppressor gene and negative regulator of β-catenin signaling, APC, wasanother member of the signaling class. Classes containing two membersincluded RNA binding, mitochondrial respiration, cytoskeletal matrix,metabolism, cell cycle, and adhesion. Single member classes includedcomplement casasde (MASP1), drug resistance (MVP), glycosaminoglycancatabolism, heparin sulfate synthesis (EXTL2), and vesicular transport(TSC1). Four genes of unknown function were also identified assignificantly upregulated in MM. TABLE 4 The 50 Most SignificantlyDownregulated Genes In Multiple Myeloma In Comparison With Normal BoneMarrow Plasma Cells Gene Chi WRS^(‡) Accession* Function Symbol Square PValue L36033 cxc chemokine SDF1 48.45 3.05 × 10⁻¹² M63928 signalingTNFRSF7 48.45 6.35 × 10⁻¹¹ U64998 ribonuclease RNASE6 46.44 2.82 × 10⁻⁹M20902 lipoprotein signaling APOC1 45.62 4.63 × 10⁻¹⁰ M26602 immunityDEFA1 40.75 1.06 × 10⁻¹² M21119 immunity LYZ 40.73 6.24 × 10⁻¹⁰ M14636metabolism PYGL 39.84 1.15 × 10⁻¹⁰ M26311 calcium signaling S100A9 38.963.60 × 10⁻¹³ M54992 signaling CD72 36.14 2.40 × 10⁻⁹ X16832 proteindegradation CTSH 35.26 1.81 × 10⁻¹² M12529 lipoprotein signaling APOE34.50 3.95 × 10⁻¹⁴ M15395 adhesion ITGB2 34.02 1.74 × 10⁻¹³ Z74616extracellular matrix COL1A2 34.02 8.06 × 10⁻¹¹ HT2152 receptor signalingCD163 33.01 1.66 × 10⁻¹² U97105 pyrimidine DPYSL2 32.52 2.22 × 10⁻¹⁰metabolism U81787 signaling WNT10B 32.50 1.77 × 10⁻⁵ HT3165 receptortyrosine AXL 31.36 5.26 × 10⁻¹¹ kinase M83667 transcription CEBPD 31.194.69 × 10⁻¹⁰ L33930 receptor signaling CD24 30.33 1.56 × 10⁻¹⁴ D83657calcium signaling S100A12 29.91 6.58 × 10⁻⁸ M11313 proteinase inhibitorA2M 29.91 1.07 × 10⁻¹⁰ M31158 signaling PRKAR2B 29.91 2.20 × 10⁻⁹ U24577lipoprotein signaling PLA2G7 29.78 2.08 × 10⁻¹⁰ M16279 adhesion MIC228.75 8.01 × 10⁻¹¹ HT2811 cell cycle CDK8 28.32 6.53 × 10⁻⁹ M26167 cxcchemokine PF4V1 27.35 4.68 × 10⁻¹¹ U44111 metabolism HNMT 27.24 2.07 ×10⁻¹¹ X59871 transcription TCF7 26.79 7.16 × 10⁻¹⁰ X67235 transcriptionHHEX 25.21 2.07 × 10⁻¹⁰ U19713 calcium signaling AIF1 25.21 2.57 × 10⁻¹⁰Y08136 apoptosis ASM3A^(†) 24.74 3.30 × 10⁻⁸ M97676 transcription MSX124.58 9.80 × 10⁻⁵ M64590 house keeping GLDC 24.27 4.10 × 10⁻⁸ M20203protease ELA2 24.03 6.36 × 10⁻¹² M30257 adhesion VCAM1 23.42 1.71 ×10⁻¹⁰ M93221 mediates MRC1 23.30 1.15 × 10⁻⁷ endocytosis S75256lipoprotein signaling LCN2 23.30 4.17 × 10⁻⁷ U97188 RNA binding KOC1^(†)22.47 5.86 × 10⁻⁹ Z23091 adhesion GP5 22.47 7.58 × 10⁻⁷ M34344 adhesionITGA2B 21.99 8.00 × 10⁻⁸ M25897 cxc chemokine PF4 21.89 1.12 × 10⁻⁸M31994 house keeping ALDH1A1 21.36 4.86 × 10⁻⁹ Z31690 lipoproteinsignaling LIPA 20.67 1.50 × 10⁻⁹ S80267 signaling SYK 20.42 5.90 × 10⁻⁵U00921 signaling LY117 18.83 1.57 × 10⁻⁸

[0117] TABLE 5 The 70 Most Significantly Upregulated Genes in MultipleMyeloma in Comparison with Normal Bone Marrow Plasma Cells Gene ChiWRS^(‡) Accession* Function Symbol^(†) Square P Value U09579 cell cycleCDKN1A 53.33 3.65 × 10⁻¹¹ U78525 amino acid synthesis EIF3S9 49.99 2.25× 10⁻² HT5158 DNA synthesis GMPS 47.11 4.30 × 10⁻¹² X57129 histone H1F246.59 5.78 × 10⁻¹³ M55210 adhesion LAMC1 45.63 1.34 × 10⁻⁹ L77886signaling, PTPRK 45.62 5.42 × 10⁻¹⁰ phosphatase U73167 glycosaminoglycanHYAL3 44.78 1.07 × 10⁻¹⁰ catabolism X16416 oncogene, kinase ABL1 43.103.96 × 10⁻¹⁴ U57316 transcription GCN5L2 43.04 1.36 × 10⁻¹² Y09022protein NOT56L^(†) 42.05 5.53 × 10⁻¹⁰ glycosylation M25077 RNA bindingSSA2 41.26 1.69 × 10⁻⁷ AC002115 mitochondrial COX6B 41.16 2.16 × 10⁻⁸respiration Y07707 transcription NRF^(†) 37.59 4.79 × 10⁻¹⁰ L22005protein ubiquination CDC34 34.50 2.89 × 10⁻⁶ X66899 transcription EWSR134.39 4.23 × 10⁻⁸ D50912 RNA binding RBM10 33.93 2.61 × 10⁻⁶ HT4824amino acid synthesis CBS 33.77 1.49 × 10⁻⁶ U10324 transcription ILF333.33 3.66 × 10⁻¹¹ AD000684 oncogene, USF2 32.18 7.41 × 10⁻¹¹transcription U68723 cell cycle CHES1 31.68 1.03 × 10⁻⁶ X16323signaling, growth HGF 30.67 4.8210⁻⁹ factor U24183 metabolism PFKM 30.478.92 × 10⁻¹⁰ D13645 unknown KIAA0020^(†) 30.47 7.40 × 10⁻⁶ S85655signaling, growth PHB 29.37 1.32 × 10⁻⁸ arrest X73478 signaling, PPP2R428.32 6.92 × 10⁻⁹ phosphatase L77701 mitochondrial COX17 27.81 1.33 ×10⁻⁶ respiration U20657 oncogene, USP4 27.71 2.31 × 10⁻⁶ proteasomeM59916 signaling, DAG SMPD1 27.49 3.52 × 10⁻⁸ signaling D16688 oncogene,DNA MLLT3 27.24 6.97 × 10⁻¹³ binding X90392 DNA endonuclease DNASE1L126.98 4.72 × 10⁻⁷ U07424 amino acid synthesis FARSL 26.93 1.66 × 10⁻⁶X54199 DNA synthesis GART 26.57 9.61 × 10⁻¹¹ L06175 unknown P5-1^(†)26.57 5.16 × 10⁻⁷ M55267 unknown EVI2A 25.92 3.79 × 10⁻⁶ M87507 proteindegradation CASP1 25.78 5.46 × 10⁻⁷ M90356 transcription BTF3L2 25.789.68 × 10⁻⁸ U35637 cytoskeletal matrix NEB 25.40 9.15 × 10⁻⁶ L06845amino acid synthesis CARS 25.34 5.39 × 10⁻⁸ U81001 DNA, nuclear SNURF24.58 4.54 × 10⁻⁵ matrix attachment U76189 heparan sulfate EXTL2 24.587.28 × 10⁻⁶ synthesis U53225 protein trafficking SNX1 24.48 5.53 × 10⁻⁷X04366 protein degradation CAPN1 24.35 1.26 × 10⁻⁹ U77456 proteinfolding NAP1L4 24.27 4.23 × 10⁻¹⁰ L42379 signaling, growth QSCN6 24.271.28 × 10⁻¹⁰ arrest U09578 signaling, kinase MAPKAPK3 24.27 2.35 × 10⁻⁹Z80780 histone H2BFH 24.27 3.44 × 10⁻¹² HT4899 oncogene, MYC 24.27 1.77× 10⁻⁵ transcription M74088 signaling, b-catenin APC 23.94 1.50 × 10⁻⁵regulator X57985 histone H2BFQ 23.90 3.25 × 10⁻¹² X79882 drug resistanceMVP 23.47 1.77 × 10⁻¹¹ X77383 protein degradation CTSO 23.18 4.68 × 10⁻⁷M91592 transcription ZNF76 23.16 1.12 × 10⁻⁸ X63692 DNA DNMT1 23.12 5.15× 10⁻¹¹ methyltransferase M60752 histone H2AFO 21.60 1.46 × 10⁻⁸ M96684transcription PURA 21.25 4.54 × 10⁻⁵ U16660 metabolism ECH1 21.18 5.52 ×10⁻⁵ M86737 DNA repair SSRP1 20.60 2.62 × 10⁻⁵ U35113 histonedeacetylase MTA1 20.60 6.67 × 10⁻¹⁰ X81788 unknown ICT1 20.42 2.97 ×10⁻⁷ HT2217 signaling MUC2A 20.33 2.61 × 10⁻⁷ M62324 unknown MRF-1^(†)20.31 3.98 × 10⁻⁹ U09367 transcription ZNF136 20.30 7.72 × 10⁻⁹ X89985cytoskeletal matrix BCL7B 19.81 5.50 × 10⁻⁹ L19871 transcription ATF319.43 1.13 × 10⁻⁶ repression X69398 adhesion CD47 19.16 6.44 × 10⁻⁷X05323 signaling macro- MOX2 19.16 8.58 × 10⁻⁶ phage inhibitor X04741protein ubiquination UCHL1 19.14 9.76 × 10⁻⁵ D87683 vesicular transportTSC1 19.12 6.81 × 10⁻⁶ D17525 complement cascade MASP1 18.81 4.05 × 10⁻⁷

EXAMPLE 9

[0118] Altered Expression of 14 Genes Differentiates Malignant fromNormal Plasma Cells

[0119] The present invention also sought to determine whether expressionpatterns of a minimum number of genes could be used to clearlydifferentiate normal, pre-neoplastic and malignant plasma cells. Amultivariate step-wise discriminant analysis (MSDA) was applied to thetop 50 significantly differentially expressed genes across the normalplasma cells (N=26) and multiple myeloma plasma cells (N=162) and alinear discriminant function between the normal plasma cell group andmultiple myeloma group was observed. Both forward and backward variableselections were performed. The choice to enter or remove variables wasbased on a Wilks' λ analysis, defined as follows: λ(x)=det W(x)/det T(x)where W(x) and T(x) are the within-group and total scatter matricesrespectively. Wilks' λ can assume values ranging from 0 to 1. Thesignificance of change in λ was tested using the F statistic. At the endof multivariate step-wise discriminant analysis, only 14 genes wereselected to compute the canonical discriminant functions (Table 6). Themultivariate step-wise discriminant analysis selected the followingequation: Discriminantscore=HG4716×3.683−L33930×3.134+L42379×1.284+L77886×1.792+M14636×5.971−M26167×6.834+U10324×2.861−U24577×10.909+U35112×2.309+X16416×6.671−X64072×5.143+7982×5.53+Z22970×4.147+Z80780×2.64−87.795.The cutoff value was −3.3525. Values less than −3.3525 indicated thesample belonged to the normal group and values greater than −3.3525indicated the sample belonged to the multiple myeloma group.

[0120] The 14 gene model was then applied to a training group consistingof 162 multiple myeloma and 26 normal plasma cell (data not shown). Across-validation analysis was performed where samples were removed oneat a time from the sample set, and training statistics and expressionmeans for each class of the modified sample set were re-calculated. Apredictive value using genes with a P value <0.05 in the modified sampleset was generated. A 100% accurate prediction of the sample types in thetraining group was obtained.

[0121] A validation group was then applied to the model. Themultivariate step-wise discriminant analysis correctly classified 116 of118 (98.31%) primary multiple myeloma samples and 8 of 8 (100%) of humanmyeloma cell lines as multiple myeloma. In addition, 6 of 6 normalplasma plasma cell samples were classified as normal. Importantly, themodel predicted that 6 of 7 monoclonal gammopathy of undeterminedsignificance cases were multiple myeloma with 1 monoclonal gammopathy ofundetermined significance case predicted to be normal (FIG. 5). Theclassification of the 6 monoclonal gammopathy of undeterminedsignificance cases as multiple myeloma has important ramifications inthat it suggests that cells in this benign condition have strongsimilarities to fully transformed cells. These results also haveimportant implications in the eitology of monoclonal gammopathy ofundetermined significance and its transition to overt multiple myeloma.The fact that the model classified monoclonal gammopathy of undeterminedsignificance as multiple myeloma is consistent with recent studies thathave shown monoclonal gammopathy of undetermined significance haschromosomal abnormalities e.g. translocations of the IGH locus anddeletion of chromosome 13 that are also common in multiple myeloma.Future studies will be aimed at identification of gene expressionpatterns that can actually distinguish monoclonal gammopathy ofundetermined significance from multiple myeloma. With the majority ofthe monoclonal gammopathy of undetermined significance cases beingclassified as multiple myeloma, the classification of a 1 monoclonalgammopathy of undetermined significance cases as normal may indicate 1)the patient does not have monoclonal gammopathy of undeterminedsignificance or 2) the monoclonal gammopathy of undeterminedsignificance cells represented a minority of the plasma cells in thesample. The monoclonal gammopathy of undetermined significance case andthe 2 multiple myeloma cases classified as normal will be followedlongitudinally to determine whether in the future the samples will shiftto the multiple myeloma group.

[0122] In order to further validate the discriminant results,two-dimensional hierarchical clustering was performed on 927 genes withexpression in at least one sample. The 118 multiple myeloma samples fromthe validation group, 32 normal plasma cells, 7 multiple myeloma celllines, and 7 monoclonal gammopathy of undetermined significance werestudied. Along the horizontal axis, experimental samples were arrangedsuch that those with the most similar patterns of expression across allgenes were placed adjacent to each other. Surprisingly, the twomisclassified multiple myelomas and one monoclonal gammopathy ofundetermined significance classified as normal plasma samples bydiscriminant analysis were also connected to the normal group in thecluster analysis (FIG. 6). This result indicated that the 14 genediscriminant model was consistent with a 927 gene hierarchical clustermodel.

[0123] A survey of the function of the 14 genes in the above analysisshowed several interesting features. The genes are not related infunction and thus represent unique and independent genetic markers thatcan clearly be used as signatures of normal and malignant cells. Genesare associated with the microenvironment (ITGB2), cell transformation(ABL1) and drug resistance (MVP). It is possible that the deregulatedexpression of these genes may represent fundamental geneticabnormalities in the malignant transformation of plasma cells. Forexample, the ITGB2 gene encodes the glycoprotein β-2 integrin (CD18)which is critical to the formation of integrin heterodimers known tomediate cell-cell and/or cell-matrix adhesion events. As plasma cellsconstitutively express ICAM-1 and this molecule can be induced on bonemarrow adherent cells, one can envisage a mechanism in which theITGB2/ICAM-1 adhesion pathway mediates adhesion among plasma cells aswell as with cells in the bone marrow microenvironment. In humanlymphomas, ITGB2 expression is found on tumor cells in low- andmedium-grade malignant lymphomas, whereas absence of ITGB2 seems to be acharacteristic of high-grade malignant lymphomas. Similar to other Blymphoma, the absence of ITGB2 might contribute to an escape fromimmunosurveillance in multiple myeloma.

[0124] In summary, the present invention describes a model that makes itpossible to diagnosis multiple myeloma by the use of the differentialexpression of 14 genes. It is currently not clear whether deregulatedexpressions of these genes are involved in the creation of the malignantphenotype or whether they represent sentinels of some underlying yet tobe recognized genetic defect(s). However, the functions of these genessuggest an underlying causal relationship between the deregulatedexpression and malignancy. TABLE 6 Fourteen Gene Defining the OptimalDiagnosis Model Wilks' Accession* Gene Symbol Lambda F to Remove Pnumber HT5158 GMPS 0.090 10.99 0.0011 L33930 CD24 0.089 8.80 0.0034L42379 QSCN6 0.087 4.24 0.0409 L77886 PTPRK 0.088 6.46 0.0119 M14636PYGL 0.091 12.62 0.0005 M26167 PF4V1 0.091 12.39 0.0005 U10324 ILF30.090 11.98 0.0007 U24577 PLA2G7 0.107 44.28 3.23 × 10⁻¹⁰ U35113 MTA10.088 6.22 0.0135 X16416 ABL1 0.099 27.65 4.04 × 10⁻⁷ X64072 ITGB2 0.09724.63 1.59 × 10⁻⁶ X79882 MVP 0.098 25.83 9.19 × 10⁻⁷ Z22970 CD163 0.0886.08 0.0146 Z80780 H2B 0.092 14.58 0.0002

EXAMPLE 10

[0125] Differential Expression of 24 Genes Can Accurately DifferentiateGene Expression-Defined Subgroups of Multiple Myeloma

[0126] The present invention also sought to determine whether expressionpatterns of a minimum number of genes could be used to clearlydifferentiate the gene expression-defined subgroups of multiple myelomaidentified with hierarchical clustering of over 5,000 genes. Asdiscussed above, two-dimensional cluster analysis of 263 multiplemyeloma cases, 14 normal plasma cells, 7 MGUS and 7 multiple myelomacell lines was performed. The sample dendrogram showed four subgroups ofMM1, MM2, MM3 and MM4 containing 50, 75, 67, and 71 patientsrespectively. Then, the top 120 statistically significant differentiallyexpressed genes as determined by Chi-square and Wilcoxon test of 31normal plasma cells and 74 newly diagnosed multiple myeloma were chosenfor use in a canonical discriminant analysis. By applying a linearregression analysis 24 genes were defined as predictors able todifferentiate the multiple myeloma subgroups (Table 7).

[0127] The 24 genes predictor model was applied to a training groupconsisting of multiple myeloma plasma cell samples located in the centerof each hierarchical clustering group [total N=129; MM1=23, MM2=33,MM3=34 and MM4=39]. A cross-validation analysis was performed on thetraining group where samples were removed one at a time from the sampleset, and training statistics and expression means for each class of themodified sample set were re-calculated. A predictive value using geneswith a P value <0.05 in the modified sample set was generated. Theresults of this analysis showed that a 100% accurate prediction of thesample types in the training group was obtained.

[0128] A validation group was then applied to the model. Themultivariate step-wise discriminant analysis correctly classified 116 of134 (86.56%) primary multiple myeloma samples into different subgroupsas compared with the subgroups defined by hierarchical clustering.Importantly, 7 of 7 (100%) of human myeloma cell lines were classifiedto MM4 as expected. In addition, the model predicted that 5 of 7 MGUScases were MM1, and the remaining cases were predicted to be MM2 and MM3respectively (FIG. 7). TABLE 7 Twenty-Four Genes Defining Subgroups ofMultiple Myeloma Accession No.* Gene Symbol Wilks' Lambda F to Remove Pvalue X54199 GART 0.004 3.13 0.0791 M20902 APOC1 0.005 4.05 0.0462X89985 BCL7B 0.005 4.47 0.0365 M31158 PRKAR2B 0.005 5.07 0.0260 U44111HNMT 0.005 5.68 0.0186 X16416 ABL1 0.005 6.72 0.0106 HT2811 NEK2 0.0058.35 0.0045 D16688 MLLT3 0.005 8.36 0.0045 U57316 CCN5L2 0.005 8.490.0042 U77456 NAP1L4 0.005 8.57 0.0040 D13645 KIAA00 0.005 9.17 0.0030M64590 GLDC 0.005 9.92 0.0020 L77701 COX17 0.005 10.01 0.0019 U20657USP4 0.005 11.10 0.0011 L06175 P5-1 0.005 11.11 0.0011 M26311 S100A90.005 11.20 0.0011 X04366 CAPN1 0.005 11.67 0.0009 AC002115 COX6B 0.00613.64 0.0003 X06182 C-KIT 0.006 13.72 0.0003 M16279 MIC2 0.006 16.120.0001 M97676 MSX1 0.006 16.41 0.0001 U10324 LIF3 0.006 19.66 0.0000S85655 PHB 0.007 20.63 0.0000 X63692 DNMT1 0.007 21.53 0.0000

EXAMPLE 11

[0129] Gene Expression “Spikes” in Subsets of Multiple Myeloma

[0130] A total of 156 genes not identified as differently expressed inthe statistical analysis of multiple myeloma versus normal plasma cells,yet highly overexpressed in subsets of multiple myeloma, were alsoidentified. A total of 25 genes with an AD spike greater than 10,000 inat least one sample are shown (Table 8). With 27 spikes, the adhesionassociated gene FBLN2 was the most frequently spiked. The gene for theinterferon induced protein 27, IFI27, with 25 spikes was the second mostfrequently spiked gene and contained the highest number of spikes over10,000 (N=14). The FGFR3 gene was spiked in 9 of the 74 cases (FIG. 2A).It was the only gene for which all spikes were greater than 10,000 AD.In fact, the lowest AD value was 18,961 and the highest 62,515, whichrepresented the highest of all spikes. The finding of FGFR3 spikessuggested that these spikes were induced by the multiplemyeloma-specific, FGFR3-activating t(4;14)(p21;q32) translocation. Totest the above hypothesis, RT-PCR for a t(4;14)(p21;q32)translocation-specific fusion transcript between the IGH locus and thegene MMSET was performed (data not shown). The translocation-specifictranscript was present in all 9 FGFR3 spike samples but was absent in 5samples that did not express FGFR3. These data suggested that the spikewas caused by the t(4;14)(p21;q32) translocation.

[0131] The CCND1 gene was spiked with AD values greater than 10,000 in13 cases. TRI-FISH analysis for the t(11;14)(q13;q32) translocation wasperformed (Table 9). All 11 evaluable samples were positive for thet(11;14)(q13;q32) translocation by TRI-FISH; 2 samples were notanalyzable due to loss of cell integrity during storage. Thus, all FGFR3and CCND1 spikes could be accounted for by the presence of either thet(4;14)(p21;q32) translocation or the t(11;14)(q13;q32) translocationrespectively.

[0132] Next, the distribution of the FGFR3, CST6, IFI27, and CCND1spikes within the gene expression-defined multiple myeloma subgroups wasdetermined (FIG. 2). The data showed that FGFR3 and CST6 spikes weremore likely to be found in MM1 or MM2 (P<0.005) whereas the spikes forIFI27 were associated with an MM3 and MM4 distribution (P<0.005). CCND1spikes were not associated with any specific subgroup (P>0.1). It isnoteworthy that both CST6 and CCND1 map to 11q13 and had no overlap inspikes. It remains to be tested whether CST6 overexpression is due to avariant t(11;14)(q13;q32) translocation. The five spikes for MS4A2(CD20) were found in either the MM1 (3 spikes) or MM2 (2 spikes)subgroups (data not shown).

[0133] The gene MS4A2 which codes for the CD20 molecule was also foundas a spiked gene in four cases (FIG. 3A). To investigate whether spikedgene expression correlated with protein expression, immunohistochemistryfor CD20 was performed on biopsies from 15 of the 74 multiple myelomasamples (FIG. 3B). All four cases that had spiked MS4A2 gene expressionwere also positive for CD20 protein expression, whereas 11 that had noMS4A2 gene expression were also negative for CD20 byimmunohistochemistry. To add additional validation to the geneexpression profiling, a comparison of CD marker protein and geneexpression in the multiple myeloma cell line CAG and the EBV-transformedlymphoblastoid cell line ARH-77 were also performed (FIG. 4). Theexpression of CD138 and CD38 protein and gene expression was high in CAGbut absent in ARH-77 cells. On the other hand, expression of CD19, CD20,CD21, CD22, CD45, and CDw52 was found to be strong in ARH-77 and absentin CAG cells. The nearly 100% coincidence of FGFR3 or CCND1 spiked geneexpression with the presence of the t(4;14)(p14;q32) ort(11;14)(q13;q32) translocation; the strong correlation of CD20 andMS42A gene expression in primary multiple myeloma; and strongcorrelation of CD marker protein and gene expression in B cells andplasma cells represent important validations of the accuracy of the geneexpression profiling disclosed herein. TABLE 8 Genes with “Spiked”Expression in Plasma Cells from Newly Diagnosed Multiple MyelomaPatients Accession Gene # of Spikes Max No.* Function Symbol Spikes >10KSpike M64347 signaling FGFR3 9 9 62,515 U89922 immunity LTB 4 2 49,261X67325 interferon IF127 25 14 47,072 signaling X59798 cell cycle CCND1 613 42,814 U62800 cysteine protease CST6 17 6 36,081 inhibitor U35340 eyelens protein CRYBB1 4 1 35,713 X12530 B-cell signaling MS4A2 5 5 34,487X59766 unknown AZGP1 18 4 28,523 U58096 unknown TSPY 4 1 23,325 U52513interferon IFIT4 5 2 21,078 signaling X76223 vesicular MAL 19 5 20,432trafficking X92689 O-linked GALNT3 4 1 18,344 glycosylation D17427adhesion DSC3 8 7 17,616 L11329 signaling DUSP2 14 1 15,962 L13210adhesion, LGALS3BP 8 2 14,876 macrophage lectin U10991 unknown G2^(†) 71 14,815 L10373 integral TM4SF2 4 2 14,506 membrane protein U60873unknown 137308 12 1 12,751 M65292 complement HFL1 9 1 12,718 regulationHT4215 phospholipid PLTP 23 1 12,031 transport D13168 growth factorENDRB 18 1 11,707 receptor AC002077 signaling GNAT1 21 1 11,469 M92934growth factor CTGF 4 1 11,201 X82494 adhesion FBLN2 27 7 10,648 M30703growth factor AR 5 1 10,163

[0134] TABLE 9 Correlation of CCND1 Spikes with FISH-Defined t (11; 14)(q13; q32) GC CCND1 Spike FISH Percent PCs with Cells PT* (AD value)^(†)t (11; 14) Translocation Counted P168 42,813 Yes 59% 113 P251 33,042 Yes80% 124 P91 31,030 Not done — — P99 29,862 Yes 65% 111 P85 26,737 Yes92% 124 P241 25,611 Yes 96% 114 P56 23,654 Yes 100%  106 P63 22,358 Yes98% 104 P199 18,761 Yes 60%  35 P107 15,205 Yes 100%  147 P75 14,642 Yes100%  105 P187 14,295 Yes 25% 133 P124 10,594 Not done — —

EXAMPLE 12

[0135] Endothelin B Receptor as Potential Therapeutic Target of MultipleMyeloma

[0136] As disclosed above, the present invention has identified a numberof genes that have significantly different expression levels in plasmacells derived from multiple myeloma compared to those of normal control.Genes that are significantly up-regulated or down-regulated in multiplemyeloma are potential therapeutic targets of multiple myeloma. Examplesof these genes are listed in Tables 4, 5 and 8. Among thesedifferentially expressed genes is endothelin B receptor (ENDBR). Thisgene was not expressed in normal plasma cells, but does show highlyelevated expression in a subset of myeloma. In fact, this gene nowappears to be highly expressed in between 30-40% of myeloma patients.FIG. 8 shows a comparison of ENDBR expression in normal plasma cells andin approximately 200 myeloma patients starting with P1 through P226.ENDBR was either off or highly expressed in multiple myeloma patients(FIG. 8A). Levels of ENDBR expression levels were approximately the samein newly diagnosed and previously treated patients, suggesting that theactivation is not a progression event (FIG. 8B).

[0137] Several important features of ENDBR should be noted. The ENDBRgene is located on chromosome 13. This is of potential significancegiven that abnormalities in chromosome 13 such as translocation ordeletions represent one of the most powerful negative risk factors inmultiple myeloma. Thus, it is possible that the hyperactivation of ENDBRexpression could be an indicator of poor prognosis for multiple myeloma.There are also extensive reports linking endothelin signaling to cellgrowth, and endothelins have been shown to activate several keymolecules with documented pathological roles in plasma celltumorigenesis. Of note are the c-MYC oncogene, a gene that is activatedin 100% of mouse plasmacytomas and hyperactivated in many primary humanmyeloma cells, and IL-6 which is a major growth and survival factor formyeloma cells. The endothelins also appear to exert their signalingthrough the phospholipase C pathway, a major signaling pathway inB-cells. Moreover, a recent paper reported that blocking endothelinsignaling resulted in inhibition of the proliferation of Kaposi'ssarcoma cells.

[0138] When the tumor cells of multiple myeloma patients were taken outof the microenvironment of bone marrow, the tumor cells did not appearto express endothelins genes in a large proportion of the population.They lack expression of the endothelin 1, 2 and 3 in most cases.However, when the myeloma cells were taken out of the bone marrow andcultured for 48-72 hours on proprietary feeder layer that mimics thebone marrow microenvironment, endothelin 1 gene expression was massivelyup-regulated in both the myeloma cells P323 and P322 as well as thefeeder layer (FIG. 9). Hence, a major variable within multiple myelomamay be the availability of endothelins. Enhanced production ofendothelins coupled with up-regulated expression of ENDBR in local areasmay contribute to the neoplastic phenotype of multiple myeloma, andblocking endothelins and endothelin receptor interaction may disrupt thedevelopment of the malignant phenotype.

EXAMPLE 13

[0139] Comparative Gene Expression Profiling of Human Plasma CellDifferentation

[0140] Examples 13-15 describe global gene expression profiling thatreveals distinct changes in transcription associated with human plasmacell differentiation. Data presented below demonstrate for the firsttime that highly purified plasma cells could be isolated from two uniquehematopoietic organs, tonsil and bone marrow. This purification ofmillions of cells eliminated background “noise” from non-specific celltypes (see FIG. 10), thereby allowing accurate genetic profile andcharacterization of these samples using highly sensitive gene expressionprofiling technology. The results disclosed herein characterizedmolecular transcription changes associated with different cell stagesand especially distinguishing differences in plasma cell, a cellpreviously thought to represent an end-stage differentiation productbased on morphological criterion.

[0141] The CD19⁺ tonsil B cells and CD138⁺ plasma cells isolated fromtonsil and bone marrow used in the study represent homogeneouspopulations with unique phenotypic characteristics. Thus, resultspresented are based on well-characterized cells as shown by flowcytometry, morphology, and expression of cIg. These results areimportant little is known about plasma cells, most likely due to theirscarcity with most previous studies focusing only on flow cytometriccharacterizations.

[0142] Another unique finding from the results is that B cells andplasma cells segregated into two branches using a hierarchical geneexpression cluster analysis. Further, within the plasma cell branch,tonsil plasma cells could be distinguished from bone marrow plasmacells, indicating that the cells represent unique stages of developmentas suspected from their derivation from unique hematopoietic organs.Genes identified herein (e.g., cell surface markers and transcriptionfactors) matched those previously identified as distinguishinglate-stage B cell development. In addition to the novel genes found,previously identified genes followed expected patterns of up- anddown-regulation and matched those genes already shown to be linked toplasma cell differentiation or essential transcription factors forplasma cell differentiation.

[0143] Although cells at distinct stages of B cell development expressCD19, it is likely that the majority of the tonsil B cells studied hererepresent germinal center centroblasts. It is known that centrocytes andcentroblasts of germinal centers can be differentiated based on theexpression of CD44 (centrocytes, CD44⁺; centroblasts, CD44⁻). Expressionof the CD44 gene was undetectable in the tonsil B cell samples used inthis study. In addition, the high level of expression of genes linked toproliferation, e.g. MKI67, PCNA, and CCNB1 (data not shown) suggestsblasts make up the largest population of cells among the tonsil B cells.Finally, MYBL, whose expression is a marker of CD38⁺ CD39⁻ centroblasts,was found to be highly expressed in the tonsil B cells, down-regulatedin tonsil plasma cells (P=0.00068), and extinguished in bone marrowplasma cells. Because centroblasts have already undergone switchrecombination, the tonsil B cells studied here represent an optimal latestage B cell population to use in a comparative study of gene expressionchanges associated with early plasma cell differentiation.

[0144] A representative analysis of normal cell types used in this studyis presented in FIG. 10. FACs analysis of the tonsil preparations beforesorting indicated that CD20^(hi)/CD38^(lo) cells represented 70% andCD38⁺/CD20⁻ cells represented 30% of the population (FIGS. 10a, b).After anti-CD19 immunomagnetic bead selection, the CD20^(hi)/CD38^(lo/−)cells were enriched to 98% and the CD38⁺/CD20⁻, CD138⁻/CD20⁺, andCD138⁻/CD38⁺ fractions represented 1% of the population (FIGS. 10b, c,e, f). Cell morphology of the purified fraction also showed that themajority of cells had typical B cell morphology (FIG. 10g).Immunofluorescence microscopy with anti-kappa and anti-lambda antibodiesindicated a slight contamination with cIg⁺ CD19⁺ cells (FIG. 10h).

[0145] Before tonsil plasma cell isolation, FACs analysis of the tonsilmononuclear fractions indicated that CD38^(hi)/CD45⁻(FIG. 10i) andCD138^(hi)/CD45⁻ cells (FIG. 10j) represented 2.4% of the population.After anti-CD138 immunomagnetic bead sorting, cells with a plasma cellphenotype that was either CD38^(hi)/CD45^(lo) (95%),CD138^(hi)/CD45^(lo) (94%), CD38^(h)/CD20^(lo) (91%), orCD138^(hi)/CD38^(hi) (92%) were greatly enriched (FIGS. 10k, l, m, n).The tonsil CD138-selected cells were also found to have a typical plasmacell morphology with increased cytoplasmic to nuclear ratio of prominentperinuclear Hoff or endoplasmic reticulum (FIG. 10o) and >95% of thecells were cIg positive (FIG. 10p).

[0146] FACs analysis prior to anti-CD138 immunomagnetic bead sorting ofbone marrow mononuclear cell samples showed similar but distinctprofiles in comparison with the tonsil preparations.CD38^(hi)/CD45^(int) and CD138^(hi)/CD45^(int) fractions showed morecells with lower expression of CD45 and a higher percentage of CD138⁺cells in the bone marrow plasma cells (FIGS. 10q, r). FACS analysisafter purification showed that the CD38^(hi)/CD45⁻ andCD38^(hi)/CD20^(lo) cells were enriched to 99% and 91%, respectively(FIGS. 10s, u). Differences between tonsil plasma cells and bone marrowplasma cells after sorting were also evident, in that whereas the tonsilplasma cells had clear evidence of CD38⁺/CD45⁺ and CD38⁺/CD20⁺ cells,these fractions were greatly reduced in the bone marrow CD138-selectedcells. Bone marrow plasma cells also expressed higher levels of CD38than the tonsil plasma cells (FIGS. 10s, k). The CD138^(hi)/CD45⁻ andCD138^(hi)/CD38^(hi) populations represented 96% and 95% of the bonemarrow plasma cell population (FIGS. 10t, v), again with a reducedamount of CD45⁺ cells and higher percentage of CD38⁺ cells as comparedwith tonsil plasma cells. As with the tonsil plasma cells, the majorityof the bone marrow cells had plasma cell morphology (FIG. 10w) and werecIg positive (FIG. 10x). Thus, immunomagnetic bead selection resulted inthe purification of a relatively homogenous tonsil B cell population anddistinct plasma cell populations from two different organs, likelyrepresenting cells at different stages of maturation.

[0147] Having demonstrated the phenotypic characteristics of the cells,the global mRNA expression was then analyzed in 7 tonsil B cell, 11tonsil plasma cell, and 31 bone marrow plasma cell samples using theAffymetrix high-density oligonucleotide microarray interrogatingapproximately 6800 named and annotated genes. The mean value of the ADexpression level of genes for the CD markers used in the cell analysis,as well as other CD markers, chemokine receptors, apoptosis regulator,and a panel of transcription factors were analyzed across the normalsamples (Table 10). CD45 was found to be highly expressed on tonsil Bcells, with lower expression on tonsil plasma cells, and absent on bonemarrow plasma cells. The genes for CD20, CD79B, CD52, and CD19 showedCD45-like expression patterns with progressive down-regulation fromtonsil B cells to tonsil plasma cells. Although CD21 showed nosignificant change from tonsil B cells to tonsil plasma cells, the genewas down-regulated in bone marrow plasma cells. CD22, CD83, and CD72showed progressive down-regulation.

[0148] Consistent with the FACS analysis, Syndecan-1 (CD138) and CD38,key plasma cell differentiation antigens, were absent or weaklyexpressed on tonsil B cells, with intermediate levels on tonsil plasmacells, and highest expression on bone marrow plasma cells. Theintermediate level of CD138 expression is likely a direct reflection ofthe heterogeneous mixture of CD138⁺ cells in the tonsil plasma cellfraction (see above) with some cells being highly CD138⁺ and othersweakly positive but still able to be sorted based on surface expressionof CD138. CD38 expression showed the progressive increase seen withCD138 in the normal cells.

[0149] It was also observed that the CD63 gene was significantlyup-regulated in bone marrow plasma cells. This is the first indicationthat this marker may be differentially regulated during plasma celldifferentiation. The gene for CD27 showed significant up-regulation fromthe B cell to tonsil plasma cell transition, whereas bone marrow plasmacells and tonsil plasma cells showed similar levels.

[0150] Transcription factors differentially expressed in plasma celldevelopment showed the expected changes. IRF4 and XBP1 weresignificantly up-regulated in tonsil and bone marrow plasma cells andCTIIA, BCL6, and STAT6 were down-regulated in the plasma cell samples.BSAP (PAX5) did not show the expected changes, but it is believed thatthis was due to an ineffective probe set for the gene because the BSAPtarget gene, BLK, did show the expected down-regulation in the tonsiland bone marrow plasma cells. Interestingly, whereas MYC showedsignificant down-regulation in the tonsil B cell to tonsil plasma celltransition, the gene was reactivated in bone marrow plasma cells tolevels higher than seen in the tonsil B cells. Whereas the chemokinereceptors CXCR4 and CXCR5 showed down-regulation in the tonsil B cell totonsil plasma cell transition, CXCR4 showed a MYC-like profile in thatthe gene was reactivated in bone marrow plasma cells. The BCL2 homologueBCL2A1 also showed the expected changes. Thus, gene expression patternsof cell surface markers are consistent with phenotypic patterns andgenes known to be strongly associated with plasma cell differentiationshowed anticipated patterns. These data support the notion that thetonsil B cells, tonsil plasma cells, and bone marrow plasma cellsrepresent distinct stages of B-cell differentiation and that geneexpression profiling of these cells can be used to gain a betterunderstanding of the molecular mechanisms of differentiation. TABLE 10Gene Expression Of CD Marker And Proteins Known To Be Differen- tiallyExpressed During Plasma Cell Differentiation Accession Symbol TBC TPCBPC Y00062 CD45 11495 ± 2198 4979 ± 2522  1385 ± 706 M27394 CD20 23860 ±5494 3799 ± 2977  289 ± 358 M89957 CD79B 14758 ± 3348 4696 ± 2440  1243± 1357 X62466 CD52 14576 ± 2395 4348 ± 2074  2831 ± 1002 M84371 CD1912339 ± 1708 6174 ± 1345  2852 ± 852 M26004 CD21  8909 ± 1640 5434 ±4053  458 ± 140 X59350 CD22 10349 ± 1422 5356 ± 1610  1929 ± 612 Z11697CD83  9201 ± 1900 2380 ± 1087  392 ± 403 M54992 CD72  6177 ± 1620 865 ±554  454 ± 548 Z48199 CD138  719 ± 519 9935 ± 3545 24643 ± 6206 D84276CD38 3122 ± 967 9833 ± 3419 14836 ± 3462 X62654 CD63 2310 ± 431 6815 ±1582 16878 ± 3305 M63928 CD27  6235 ± 1736 15937 ± 6691  16714 ± 4442M31627 XBP1 12978 ± 1676 54912 ± 13649 49558 ± 10798 U52682 IRF4 1863 ±630 8422 ± 3061 11348 ± 3118 U00115 BCL6  7979 ± 1610 3303 ± 2070  618 ±335 X74301 CIITA 1553 ± 263 236 ± 217  113 ± 82 U16031 STAT6 1314 ± 512386 ± 335  191 ± 187 S76617 BLK  3654 ± 1551 388 ± 592   95 ± 86 X68149CXCR5  3381 ± 1173 183 ± 299   92 ± 183 U29680 BCL2A1  3290 ± 1073 1121± 817   483 ± 209 L00058 MYC 1528 ± 474 348 ± 239  2103 ± 903 L06797CXCR4 11911 ± 2093 6673 ± 3508 18033 ± 5331

EXAMPLE 14

[0151] Identification of Differentially Expressed Genes in the Tonsil BCell to Tonsil Plasma Cell and in the Tonsil Plasma Cell to Bone MarrowPlasma Cell Transitions

[0152] A more detailed and comprehensive evaluation was performed todetermine gene expression changes that accompany the transition oftonsil B cells to tonsil plasma cells and the changes that occur as theimmature tonsil plasma cells exit the lymph node germinal center andmigrate to the bone marrow. To reveal global expression distinctionsamong the samples, hierarchical cluster analysis was performed with 4866genes in 7 tonsil B cell, 7 tonsil plasma cell, and 7 bone marrow plasmacell cases (FIG. 11). As expected, this analysis revealed a majordivision between the tonsil B cell samples and plasma cell samples withthe exception of one tonsil plasma cell sample being clustered withtonsil B cell. The normal plasma cells were further subdivided into twodistinct groups of tonsil plasma cells and bone marrow plasma cells.Thus, global gene expression patterns confirmed the segregation oftonsil plasma cells and bone marrow plasma cells and also allowed thedistinction of tonsil B cells from both plasma cell types.

[0153] χ² and Wilcoxon rank sum analysis were used to identify 359 and500 genes whose mRNA expression levels were significantly altered(P<0.00005) in the tonsil B cell to tonsil plasma cell and tonsil plasmacell to bone marrow plasma cell comparison, respectively. Genes thatwere significantly differentially expressed in the tonsil B cell totonsil plasma cell transition were referred as “early differentiationgenes” (EDGs) and those differentially expressed in the tonsil plasmacell to bone marrow plasma cell transition were referred as “latedifferentiation genes” (LDGs).

[0154] Early Differentiation Genes

[0155] Of the top 50 EDGs (Table 11), most of the genes (43) weredown-regulated with only 7 genes being up-regulated in this transition.Gene expression was described as being at 1 of 5 possible levels. AnAAC, indicating an undetectable or absent gene transcript, was definedas “−”. For all the samples in a group, expression levels were definedas “+” if the gene transcript was present and the AD was <1000, “++” for1000≦AD<5000, “+++” for 5000≦AD<10,000, and “++++” for AD≧10,000. Thelargest group of EDGs encoded transcription factors. Of 16 transcriptionfactors, only 3, XBP-1, IRF4 and BMI1, were up-regulated EDGs. Among thedown-regulated transcription factors, MYC and CIITA were found. Thelargest family included four ets domain-containing proteins: ETS1, SPIB,SPI1, and ELF1. Other transcription factors included the repressors EEDand ID3, as well as the activators RUNX3, ICSBP1, REL, ERG3, and FOXM1.It is of potential significance that as IRF4 is up-regulated in both thetonsil B cell to tonsil plasma cell and tonsil plasma cell to bonemarrow plasma cell transitions, the IRF family member interferonconsensus sequence binding protein, ICSBP1, which is a lymphoid-specificnegative regulator, was the only gene that was expressed at a +++ levelin tonsil B cells and was shut down in both tonsil plasma cells and bonemarrow plasma cells. These results suggest that the removal of ICSBP1from IRF binding sites may be an important mechanism in regulating IRF4function.

[0156] The second most abundant class of EDGs code for proteins involvedin signaling. CASP10 which is involved in the activation cascade ofcaspases responsible for apoptosis execution represented the onlysignaling protein up-regulated in tonsil plasma cells.

[0157] Three small G proteins, the Rho family members ARHG and ARHH, andthe proto-oncogene HRAS were down-regulated EDGs. Two members of thetumor necrosis factor family TNF and lymphotoxin beta (LTB), as well asthe TNF receptor binding protein were LDGs. Given the important role ofIL-4 in triggering class-switch recombination, the observation ofdown-regulation (tonsil B cell to tonsil plasma cell), and eventualextinguishing (tonsil plasma cell to bone marrow plasma cell) of IL4Rfits well with the differentiation states of the cells under study.

[0158] Finally, the down-regulation of the B lymphoid tyrosine kinase(BLK) whose expression is restricted to B lymphoid cells and mayfunction in a signal transduction pathway suggests that the reduction ofthis kinase is important in the early stages of plasma celldifferentiation. Given the important role of cell adhesion in plasmacell biology, up-regulation of ITGA6 and PECAM1 could be of particularimportance. In fact, these genes also showed a continual up-regulationin the tonsil plasma cell to bone marrow plasma cell transition andrepresented the only extracellular adhesion genes in the EDG class.Other multiple-member classes of down-regulated EDGs included thoseinvolved in cell cycle (CCNF, CCNG2, and CDC20) or DNA repair/maintenance (TERF2, LIG1, MSH2, RPA1). The down-regulation of thesegenes may thus be important to inducing and/or maintaining the terminaldifferentiated state of the plasma cells.

[0159] Late Differentiation Genes

[0160] In the top 50 LDGs, 33 were up-regulated or turned on and 17genes were down-regulated or turned off (Table 12). Although 16 EDGswere transcription factors, only 5 LDGs belonged to this class. The BMI1gene, which was an up-regulated EDG, was also an LDG, indicating thatthe gene undergoes a significant increase in expression in both thetonsil B cell to tonsil plasma cell and tonsil plasma cell to bonemarrow plasma cell transitions. BMI1 was the only up-regulatedtranscription factor. MYBL1, MEF2B, and BCL6 were shut down in bonemarrow plasma cells and the transcription elongation factor TCEA1 wasdown-regulated. The largest class of LDG (n=16; 11 up- and 5down-regulated) coded for proteins involved in signaling. The LIMcontaining protein with both nuclear and focal adhesion localization,FHL1; and the secreted proteins, JAG1, a ligand for Notch, insulin-likegrowth factor IGF1; and bone morphogenic protein BMP6 were up-regulated.The dual specific phosphatase DUSP5 and the chemokine receptor CCR2represented genes with the most dramatically altered expression and wereturned on to extremely high levels in bone marrow plasma cells whilebeing absent in tonsil plasma cells. Additional signaling genes,including the membrane cavealoe, CAV1 and CAV2, plasma membrane proteinsimportant in transportation of materials and organizing numerous signaltransduction pathways, were up-regulated LDGs.

[0161] Given the dramatic difference in life spans of tonsil plasmacells (several days) and bone marrow plasma cells (several weeks tomonths), the up-regulation of the anti-apoptotic gene BCL2 (− in tonsilB cells and ++ in bone marrow plasma cells) and concomitantdown-regulation of the apoptosis-inducing protein BIK (+++ in tonsil Bcells and − in bone marrow plasma cells) may be critical in regulatingnormal programmed cell death. As in the EDGs, LDGs contained multipleadhesion-related genes, and, as in the EDGs, the LDG adhesion genes wereall up-regulated.

[0162] The PECAM1 gene was found to be both an EDG and LDG, suggestingthat a gradation of cell surface expression of this gene is critical indevelopment. Whereas the integrin family member ITGA6 was an EDG, ITGA4was found to be an LDG. The finding that ITGA4 or VLA-4 (very lateantigen 4) was an LDG is consistent with published data showing thatthis integrin is most predominant on late stage plasma cells. Theadhesion molecule selectin P ligand (SELPLG) which mediates highaffinity, calcium-dependent binding to P-, E- and L-selectins, mediatingthe tethering and rolling of neutrophils and T lymphocytes onendothelial cells, may facilitate a similar mechanism in late stageplasma cells. In addition, the epithelial membrane protein 3 (EMP3), aintegral membrane glycoprotein putatively involved in cell-cellinteractions, was identified. LRMP (JAW1), a lymphoid-restricted,integral ER membrane protein based on strong homology to MRVI1 (IRAG)and is likely a essential nitric oxide/cGKI-dependent regulator ofIP3-induced calcium release from endoplasmic reticulum stores, was foundto be a down-regulated LDG. The discovery of LRMP as a down-regulatedLDG is consistent with previous studies showing that, although highlyexpressed in lymphoid precursors, it is shut down in plasma cells.

[0163] Thus, the gene expression profiling results confirmed previousobservations as well as identified novel and highly significant changesin mRNA synthesis when tonsil B cells and tonsil plasma cells and tonsilplasma cells and bone marrow plasma cells are compared. TABLE 11Early-Stage Differentiation Genes: Top 50 Differentially Expressed GenesIn Comparison Of Tonsil B Cells And Tonsil And Bone Marrow Plasma CellsQuantitative Gene Expression Accession Symbol Function TBC TPC BPCU60519 CASP10 apoptosis − + ++ X53586 ITGA6 adhesion − + ++ U04735 STCHchaperone + ++ ++ L13689 BMI1 transcription; repressor; + ++ +++ PcGL34657 PECAM1 adhesion + ++ +++ U52682 IRF4 transcription; IRF family ++++ +++ M31627 XBP1 transcription; bZip family +++ ++++ ++++ AB000410OGG1 DNA glycosylase + − − D87432 SLC7A6 solute transporter + − − J04101ETS1 transcription; ets family + − − L38820 CD1D immunity + − − M28827CD1C immunity + − − M55542 GBP1 signaling; GTP binding + − − M81182ABCD3 ABC transporter + − − M85085 CSTF2 mRNA cleavage stimu- + − −lating factor U74612 FOXM1 transcription; fork-head + − − family U84720RAE1 RNA export + − − V00574 HRAS signaling; GTP binding + − − proteinX02910 TNF signaling; TNFα + − − X63741 EGR3 transcription; egr family +− − X93512 TERF2 telomere repeat binding + − − protein Z36714 CCNF cellcycle; cyclin F + − − AB000409 MNK1 signaling; kinase + − + M33308 VCLcytoskeleton + − ++ D16480 HADHA mitochondrial oxidation ++ − − M63488RPA1 DNA replication/repair ++ − − U03911 MSH2 DNA repair ++ − − U69108TRAF5 signaling; TNFR associated ++ − − protein X12517 SNRPC mRNAsplicing ++ − − X52056 SPI1 transcription; ets family ++ − − X68149 BLR1signaling; cxc receptor ++ − − X74301 CIITA transcription; adaptor ++ −− X75042 REL transcription; rel/dorsal ++ − − family L00058 MYCtranscription; bHLHZip ++ − ++ M36067 LIG1 DNA ligase ++ + + M82882 ELF1transcription; ets family ++ + + S76617 BLK signaling; kinase ++ + +U47414 CCNG2 cell cycle; cyclin G ++ + + U61167 SH3D1B unknown; SH3containing ++ + + protein X61587 ARHG signaling; Rho G ++ + + Z35278RUNX3 transcription; contains runt ++ + + domain M91196 ICSBP1transcription; IRF family +++ − − M34458 LMNB1 cytoskeletal matrix +++ +− U90651 EED transcription; repression; +++ + + PcG X69111 ID3transcription; repression; +++ + + bHLH X52425 IL4R signaling; cytokinereceptor +++ ++ − Z35227 ARHH signaling; Rho H +++ ++ + U89922 LTBsignaling; TNF-c +++ + + U05340 CDC20 cell cycle; activator of APC ++++++ − X66079 SPIB transcription; ets family ++++ ++ −

[0164] TABLE 12 Late-Stage Differentiation Genes: Top 50 DifferentiallyExpressed Genes In Comparison Of Tonsil And Bone Marrow Plasma CellsQuantitative Gene Expression Accession Symbol Function TPC BPC U32114CAV2 signaling; membrane − + caveolae U60115 FHL1 signaling; LIM domain− + U73936 JAG1 signaling; Notch ligand − + X57025 IGF1 signaling;growth factor − + Z32684 XK membrane transport − + D10511 ACAT1metabolism; ketone − ++ Y08999 ARPC1A actin polymerization − ++ M14745BCL2 signaling; anti-apoptosis − ++ M24486 P4HA1 collagen synthesis − ++M60315 BMP6 signaling; TGF family − ++ U25956 SELPLG adhesion − ++X16983 ITGA4 adhesion − ++ Z18951 CAV1 signaling; membrane − ++ caveolaeM60092 AMPD1 metabolism; energy − +++ U15932 DUSP5 signaling;phosphatase − ++++ U95626 CCR2 signaling; chemokine − ++++ receptorD78132 RHEB2 signaling; ras homolog + ++ L41887 SFRS7 mRNA splicingfactor + ++ M23161 LOC90411^(a) unknown + ++ M37721 PAM metabolism;hormone + ++ amidation M69023 TSPAN-3^(a) unknown + ++ U02556 TCTE1Ldynein homolog + ++ U41060 LIV-1^(a) unknown + ++ U44772 PPT1 lysosomeenzyme + ++ U70660 ATOX1 metabolism; antioxidant + ++ X92493 PIP5K1Bsignaling; kinase + ++ M23254 CAPN2 cysteine protease + +++ J02763S100A6 signaling; calcium binding ++ +++ L13689 BMI1 transcription; +++++ repressor; PcG L34657 PECAM1 adhesion ++ +++ M23294 HEXB metabolism;hexoaminidase ++ +++ M64098 HLDBP metabolism; sterol ++ ++++ U52101 EMP3adhesion ++ ++++ X66087 MYBL1 transcription; myb-like + − X54942 CKS2cell cycle; kinase regulator ++ − X73568 SYK signaling; kinase ++ −L08177 EBI2 signaling; receptor ++ − M25629 KLK1 protease; serine ++ −U00115 BCL6 transcription; Zn-finger ++ − U23852 LCK signaling; kinase++ − U60975 SORL1 endocytosis ++ − X63380 MEF2B transcription; MADs box++ − L25878 EPXH1 metabolism; epoxide ++ + hydrolase Z35227 ARHHsignaling; Rho C ++ + X89986 BIK signaling; apoptosis +++ − M13792 ADAmetabolism; purine +++ + U10485 LRMP ER membrane protein +++ + M81601TCEA1 transcription; elongation +++ ++ X70326 MACMARC actin binding++++ + K X56494 PKM2 metabolism; energy ++++ +

EXAMPLE 15

[0165] Previously Identified and Novel Genes in Plasma CellDifferentiation

[0166] In this gene expression profiling study, not only previouslyidentified but also novel genes associated with plasma cell developmentwere identified. Some of the genes that may be pertinent to plasma celldifferentiation are discussed here.

[0167] Polyadenylation of mRNA is a complex process that requiresmultiple protein factors, including 3 cleavage stimulation factors(CSTF1, CSFT2 and CSTF3). It has been shown that the concentration ofCSTF2 increases during B cell activation, and this is sufficient toswitch IgM heavy chain mRNA expression from membrane-bound form tosecreted form. The CSTF2 gene was expressed at low levels in tonsil Bcells, but was turned off in tonsil and bone marrow plasma cells,indicating that CSTF2 gene expression can also be used to define plasmacell differentiation.

[0168] The gene for CD63 showed a progressive increase in geneexpression across the three cell types studied. CD63 belongs to thetransmembrane 4 super family (TM4SF) of membrane proteins. Expressionhas been found on the intracellular lysosomal membranes of hemopoieticprecursors in bone marrow, macrophages, platelets, and Wiebel-Paladebodies of vascular endothelium. Importantly, CD63 was described as amaker for melanoma progression and regulates tumor cell motility,adhesion, and migration on substrates associated with P1 integrins.

[0169] Most importantly, the discovery of novel genes reported hereinwill lead to a broader knowledge of the molecular mechanisms involved inplasma cell differentiation. Specifically, of the top 50 EDGs, most weredown-regulated, and a majority of the EDGs were transcription factors,suggesting that transcriptional regulation is an important mechanism formodulating differentiation. Among the LDGs, transcription factorrepresentation was much lower than among the EDGs.

[0170] Cell Cycle Control and Programmed Cell Death

[0171] Consistent with the terminal differentiation of plasma cells,many genes involved in cell cycle control and DNA metabolism weredown-regulated EDGs. The modulation of DNA ligase LIG1; repair enzymesMSHC, and RPA1, CDC20; and the cyclins CCNG2 and CCNF may have importantconsequences in inducing the quiescent state of plasma cells. Thetelomeric repeat binding protein TERF2, which is one of two recentlycloned mammalian telomere binding protein genes, was a down-regulatedEDG. TERF2 acts to protect telomer ends, prevents telomere end-to-endfusion, and may be important in maintaining genomic stability. It is ofinterest to determine if TERF2 is down-regulated during the terminaldifferentiation of all cell types, and whether the lack of this geneproduct in tumors of terminally differentiated cells results in the highdegree of chromosome structural rearrangements which is a hallmark ofmultiple myeloma that lacks TERF2 gene expression (unpublished data).

[0172] The CDC28 protein kinase 2 gene CKS2, which binds to thecatalytic subunit of the cyclin dependent kinases and is essential fortheir biological function, was the only cell cycle gene in the LDGgenes. It was expressed in tonsil plasma cells that are capable ofmodest proliferation; however, CKS2 was completely extinguished in bonemarrow plasma cells. Thus, shutting down CKS2 expression may be criticalin ending the proliferative capacity of bone marrow plasma cells.

[0173] A distinguishing feature of plasma cell terminal differentiationis the acquisition of increased longevity in the bone marrow plasmacells. It is likely that this phenomenon is controlled throughprogrammed cell death or apoptosis. The finding that anti-apoptotic andpro-apoptotic genes, BCL2 and BIK, demonstrated opposing shifts inexpression is consistent with these two genes playing major roles inextending the life-span of bone marrow plasma cells.

[0174] Transcription Factors

[0175] The majority of differentially expressed genes belong to thetranscription factor family. Of the 50 EDGs, only 7 were up-regulated.IRF4 and XBP1, two genes known to be up-regulated during plasma celldifferentiation were in this group. Both genes were expressed at equallevels in the tonsil and bone marrow plasma cells, suggesting that acontinual increase in expression of these important regulators does notoccur. Although not on the HuGenFL Microarray, recent studies usingthird generation AffymetrixU95Av2 microarray have also revealed aninduction of Blimp-1 (PRDM1) expression in plasma cells compared withtonsil B cells (unpublished data), confirming the expected patterns ofthese transcription factors.

[0176] The vast majority of EDGs were down-regulated and the singlelargest subgroup of EDGs represented transcription factors (13 of 43genes). Four of the 13 transcription factors, ETS1, SP11, SP1B, andELF1, belong to the ets family. These results are consistent withprevious studies showing that several of the ETS proteins (ETS1, ELF1,PU.1 (SPI1), and SPI-B) are expressed in the B cell lineage. It isinteresting to note that the down-regulation of ETS1 in the transitionbetween tonsil B cell to tonsil plasma cell may be an important switch,as ETS1 knock-out mice show dramatic increases in plasma cells in thespleen and peripheral blood. In addition, it is curious that althoughSPI1 (PU.1) interacts with IRF4 in Blimp-1⁺ germinal center tonsil Bcells and plasma cells, data presented herein show that whereas IRF4 isup-regulated in the plasma cell transition, SPI1 is shut down in tonsiland bone marrow plasma cells. Thus, these data support the notion thatthe ets family of transcription factors are important hematopoieticallyand that down-regulation of at least four family members appears to bean important event in terminal differentiation of plasma cells.

[0177] The cytoskeletal gene vinculin (VCL) and the MAPkinase-interacting serine/threonine kinase 1 gene (MKNK1) representednovel EDGs. Vinculin is thought to function in anchoring F-actin to themembrane, whereas MKNK1 is an ERK substrate that phosphorylates eIF4eafter recruitment to the eIF4F complex by binding to eIF4G. These twogenes were turned off in the tonsil B cell to tonsil plasma celltransition, but were reactivated in bone marrow plasma cells. The MYCproto-oncogene also showed a dramatic down-regulation in the tonsil Bcell to tonsil plasma cell transition with reactivation in bone marrowplasma cells. It will be important to understand if these two genes areregulated either directly or indirectly by MYC. One of the mechanisms bywhich PRDF1-BF1 promotes generation of plasma cells is repression ofMYC, thereby allowing the B cells to exit the cell cycle and undergoterminal differentiation. Instant study showing the extinguishing of MYCin the tonsil B cell to tonsil plasma cell transition is consistent withthis data. The reactivation of MYC in bone marrow plasma cells to levelssimilar to those seen in tonsil B cells, which appear to be highlyproliferative blasts, is unresolved but suggests that MYC may have dualroles.

[0178] Similar to the tonsil B cell to tonsil plasma cell transition,the majority of the transcription factors were down-regulated in thetonsil to bone marrow plasma cell transition. The BCL6 gene, althoughnot in the top 50 significant EDGs, did make the top 50 list for LDGs.BCL6 did show a progressive loss of expression from tonsil B cells totonsil plasma cells (see Table 10), but there was then a dramatic lossof expression in bone marrow plasma cells. Additional transcriptionfactors, the myb-like gene MYBL1, and the MADS box factor MEF2B, werealso turned off in bone marrow plasma cells and may be major regulatorsof the terminal stages of plasma cell differentiation. The transcriptionelongation factor TCEA1 was down-regulated but remained present. BMI1, amember of a vertebrate Polycomb complex that regulates segmentalidentity by repressing HOX genes throughout development, showed asignificant progressive increase in expression across all groups. It isof note that BMI1 is the human homolog of the mouse Bmi-1 proto-oncogeneoriginally discovered as cooperating with transgenic c-Myc in inducing Bcell lymphomas.

[0179] Given the recognition that changes in levels of expression oftranscription factors represent the most striking feature of plasma celldifferentiation, it is of interest to elucidate distinct pathways oftranscriptional regulation driven by the various classes oftranscription factors discovered herein. This can be done with the aidof global expression profiling and sophisticated data mining tools suchas Baysian networks.

EXAMPLE 16

[0180] Identification of Genes with Similar Expression between MultipleMyeloma and Cells at Different Stages of B Cell Development.

[0181] Examples 16 and 17 describe the establishment of a B celldevelopmental stage-based classification of multiple myeloma usingglobal gene expression profiling.

[0182] To classify multiple myeloma with respect to EDG and LDG reportedabove, 74 newly diagnosed cases of multiple myeloma and 7 tonsil B cell,7 tonsil plasma cell, and 7 bone marrow plasma cell samples were testedfor variance across the 359 EDGs and 500 LDGs disclosed above. The top50 EDGs that showed the most significant variance across all sampleswere defined as early differentiation genes for myeloma (EDG-MM);likewise, the top 50 LDGs showing the most significant variance acrossall samples were identified as late differentiation genes for myeloma-1(LDG-MM1). Subtracting the LDG-MM1 from the 500 LDG and then applyingone-way ANOVA test for variance to the remaining genes identified thetop 50 genes showing similarities between bone marrow plasma cells andmultiple myeloma. These genes were defined as LDG-MM2.

[0183] Within the top 50 EDG-MM (Table 13), 18 genes that showedup-regulation in the tonsil B cell to tonsil plasma cell transitionshowed down-regulation to levels at or below that seen in tonsil Bcells. The remaining 32 EDG-MM showed a reverse profile, in that thesegenes were down-regulated in the tonsil B cell to plasma celltransition, but showed tonsil B cell-like expression in multiplemyeloma. In Table 13, gene expression was described as being at 1 of 5possible levels. An absent absolute call (AAC), indicating anundetectable or absent gene transcript, was defined as “−”. For all thesamples in a group, expression levels were defined as “+” if the genetranscript was present and the average difference (AD) was <1000, “++”for 1000≦AD<5000, “+++” for 5000≦AD<10,000, and “++++” for AD≧10,000.

[0184] One of the most striking genes defining EDG-MM was the cyclindependent kinase 8 (CDK8), which was found absent in tonsil B cells butup-regulated to extremely high levels in tonsil and bone marrow plasmacells and then shut down again in virtually all multiple myeloma cases.The mitotic cyclin showed a progressive loss in expression from tonsil Bcell (++) to tonsil plasma cell (+) to bone marrow plasma cell (−),whereas multiple myeloma cases either showing bone marrow-like levels ortonsil B cell levels. Given that the tonsil B cells used in this studylikely represent highly proliferative centroblasts, multiple myelomacases with similar levels might be suggestive of a proliferative form ofthe disease. A total of 27 of the top 50 EDG-MM showed no variability inmultiple myeloma, ie, all multiple myeloma and tonsil B cell samplesshowed similar levels of expression.

[0185] A majority (34 of 50) of the top 50 LDG-MM1 (Table 14) were genesthat showed up-regulation from the transition of tonsil plasma cell tobone marrow plasma cell, but showed down-regulation to tonsil plasmacell levels in multiple myeloma. The overall pattern seen for LDG-MM1was the reverse seen for the EDG-MM, where a majority of those genesshowed down-regulation from tonsil B cell to plasma cell and upregulation to tonsil B cell-like levels in multiple myeloma. The mostdramatically altered LDG-MM1 was seen in the massive up-regulation ofthe cxc chemokines SDF1, PF4, and PPBP in bone marrow plasma cells incontrast with complete absence of detectable transcripts in all multiplemyeloma. These results are validated by the fact that two separate anddistinct probe sets interrogating different region of SDF1 (accessionnumbers L36033 and U19495) were found to show identical patterns. TheRB1 tumor suppressor gene showed a significant up-regulation in thetonsil plasma cell (+) to bone marrow plasma cell (++) transition withmultiple myeloma showing levels consistent with either cell type. Unlikewith the EDG-MM, only 15 of the top 50 LDG-MM1 showed no variabilitywithin the multiple myeloma population.

[0186] The LDG-MM2 genes (Table 15) showing similarities between bonemarrow plasma cells and subsets of multiple myeloma revealed that allgenes showed variability within multiple myeloma and that thevariability could be dramatic, e.g. the apoptosis inhibitor BIK. Unlikethose seen in EDG-MM and LDG-MM1, a large class of LDG-MM2 representedgenes coding for enzymes involved in metabolism with a majority involvedin glucose metabolism. TABLE 13 EDG-MM: Tonsil B Cell-like MultipleMycloma Genes Quantitative Gene Expression Accession Symbol Function TBCTPC BPC MM D28364 ANXA2 annexin family − + + −/+ U81787 WNT10Bsignaling; − + ++ −/++ ligand U88898 LOC51581^(a) unknown − + + −/+X12451 CTSL protease; − ++ ++ − cysteine Z25347 CDK8 cell cycle; − ++++++ −/++ kinase + + D38548 KIAA0076^(a) unknown + ++ ++ +/++ D86479AEBP1 extracellular + ++ ++ + matrix U04689 OR1D2 signaling; + ++ + +receptor M31328 GNB3 signaling; G + ++ ++ + protein U13395 WWOXmetabolism; + ++ ++ + oxidoreductase X14675 BCR signaling; + ++ ++ +GTPase for RAC X16665 HOXB2 transcription; + ++ ++ −/+ homeobox domainZ11899 POU5F1 transcription; + ++ ++ + homeobox domain Z36531 FGL2secreted + ++ ++ + fibrinogen-like X80907 PIK3R2 signaling; + +++ +++ ++kinase adaptor D31846 AQP2 aquaporin ++ +++ +++ ++ L18983 PTPRNphosphatase; ++ ++++ ++++ ++ membrane M23323 CD3E signaling; TCR ++ ++++++++ ++ partner D83781 KIAA0197^(a) unknown + − − + HT4824 CBSmetabolism; + − − −/++ cystathionine- beta-synthase S78873 RABIFsignaling; GTP + − − +/++ releasing factor U32645 ELF4 transcription; +− − −/+ ets domian X97630 EMK1 signaling; + − − + kinase; ELK domainZ24724 UN- cell cycle + − − +/++ KNOWN^(a) D16480 HADHA mitochondrial ++− − −/++ oxidation L77701 COX17 mitochondrial ++ − − −/++ oxidationM90356 BTF3L2 transcription; ++ − − ++ NAC domain U08815 SF3A3spliceosome ++ − − +/++ U53225 SNX1 intracellular ++ − − +/++trafficking M25753 CCNB1 cell cycle ++ + − −/++ D87448 TOPBP1^(a)topoisomerase ++ + + +/++ II binding protein L38810 PSMC5 26S ++ + + ++proteasome subunit 5 M29551 PPP3CB signaling; ++ + + +/++ calciumdependent pho M32886 SRI signaling; cal- ++ + + ++ cium binding U24704PSMD4 26S ++ + + ++ proteasome subunit 4 U25165 FXR1 RNA binding ++ + +++ protein U37022 CDK4 cell cycle; ++ + + ++ kinase U53003 C21orf33unknown; high- ++ + + ++ ly conserved X89985 BCL7B actin cross- ++ + +++ linking D49738 CKAP1 tubulin folding +++ + + ++ D43950 CCT5chaperonin +++ ++ ++ +++ D82348 ATIC metabolism; +++ ++ ++ +++ purinebiosynthesis D86550 DYRK1A signaling; +++ ++ ++ +++ kinase L06132 VDAC1anion channel +++ ++ ++ ++/ +++ L43631 SAFB nuclear scaf- +++ ++ ++++/+++ fold factor M30448 CSNK2B signaling; +++ ++ ++ ++/+++ caseinkinase regulation X76013 QARS metabolism; +++ ++ ++ ++/ glutaminyl +++tRNA synthetase D83735 CNN2 actin binding ++++ ++ ++ ++/+++ M86667NAP1L1 nucleosome +++ ++ ++ +++ assembly + X04828 GNAI2 signaling; G++++ ++ ++ ++/+++ protein

[0187] TABLE 14 LDG-MM1: Tonsil Plasma Cell-Like Multiple Myeloma GenesQuantitative Gene Expression Accession Symbol Function TPC BPC MM U9090223612^(a) unknown; related to − + −/++ TIAM1 D12775 AMPD3 metabolism;AMP − + −/++ deaminase U37546 BIRC3 signaling; anti- − ++ −/++ apoptosisZ11793 SEPP1 metabolism; selenium − +++ −/+ transport L36033 SDF1signaling; cxc − +++ − chemokine U19495 SDF1 signaling; cxc − +++ −chemokine M27891 CST3 protease inhibitor − ++++ −/++++ M26602 DEFA1immunity − ++++ −/++++ M25897 PF4 signaling; cxc − ++++ − chemokineM54995 PPBP signaling; cxc − ++++ − chemokine U79288 KIAA0513^(a)unknown + ++ +/++ M59465 TNFAIP1 signaling; anti- + ++ +/++++ apoptosisX53586 ITGA6 adhesion + ++ +/++ D50663 TCTEL1 dynein light chain + +++/++ U40846 NAGLU metabolism; hepran + ++ +/++ sulfate degradationM80563 S100A4 Signaling; calcium + ++ +/++++ binding X04085 CATmetabolism; catalase + ++ +/++ L02648 TCN2 metabolism; vitamin + ++ +B12 transport L35249 ATP6B2 lysosome; vacuolar + ++ + proton pump L09209APLP2 amyloid beta precursor + ++ + like L41870 RB1 cell cycle + ++ +/++X76732 NUCB2 signaling; calcium + +++ +/+++ binding D29805 5B4GALTadhesion + +++ + 1 M29877 FUCA1 lysosome; fucosidase + +++ +/++ M32304TIMP2 metalloproteinase 2 + +++ +/++++ inhibitor D10522 MACS actincrosslinking + ++++ −/++ L38696 RALY^(a) RNA binding ++ +++ ++ U05875IFNGR2 signaling; interferon ++ +++ ++/+++ gamma receptor U78095 SPINT2protease inhibitor; ++ +++ −/+++ blocks HGF L13977 PRCP lysosomal; ++++++ ++ angiotensinase C U12255 FCGRT IgG Fc receptor ++ ++++ −/+++L06797 CXCR4 signaling; SDF1 ++ ++++ ++/ receptor ++++ D82061 FABGLmetabolism ++ ++++ ++/+++ Y00433 GPX1 oxidation protection +++ ++++++/+++ M60752 H2AFA histone; nucleosome + − −/++ U18300 DDB2 DNArepair + − + X63692 DNMT1 DNA methyl- + − + transferase D11327 PTPN7signaling; phosphatase ++ − ++ X54942 CKS2 cell cycle; kinase ++ − +/+++regulator D14874 ADM adrenomedullin ++ + +/++++ D86976 KIAA0223^(a)minor histocompata- ++ + +/+++ bility antigen X52979 SNRPB mRNA splicing++ + +/++ Z49254 MRPL23 mitochondrial ++ + ++ ribosomal protein U66464HPK1 signaling; kinase ++ + +/++ U91903 FRZB signaling; WNT ++ + +/++antagonists D87453 MRPS27 mitochondrial ++ + +/++ ribosomal proteinX59932 CSK signaling; kinase +++ ++ ++ L17131 HMGIY transcription; high++++ + ++/ mobility group ++++ L19779 H2AFO histone; nucleosome ++++ ++++++ U70439 SSP29^(a) unknown ++++ +++ +++/ ++++

[0188] TABLE 15 LDG-MM2: Bone marrow Plasma Cell-like Multiple MyelomaGenes Quantitative Gene Expression Accession Symbol Function BPC MMU61145 EZH2 transcription; SET domain − −/+ HT4000 SYK signaling;lymphocyte − −/++ kinase X89986 BIK signaling; apoptosis inducer −−/++++ D85181 SC5DL metabolism; sterol- + −/+ C5-desaturase M98045 FPGSmetabolism; folylpoly- + −/++ glutamate synthase L41559 PCBDtranscription; enhances + −/++ TCF1 activity L25876 CDKN2 cell cycle;CDK inhibitor; + +/++ phosphatase U76638 BRAD1 transcription; BRCA1 ++/++ heterodimer L05072 IRF1 transcription; IRF family + +/++ D87440KIAA025^(a) unknown + +/++ U02680 PTK9 tyrosine kinase + +/++ U28042DDX10 oncogene; ATP-dependent + +/++ RNA helicase L20320 CDK7 cellcycle; kinase + +/++ X56494 PKM2 metabolism; pyruvate + +/++++ kinaseM12959 TCRA signaling; T cell receptor ++ −/++ HT3981 INSL3 signaling;insulin-like ++ −/++ peptide; IGF family U21931 FBP1 metabolism;fructose ++ −/++++ bisphophatase Z48054 PXR1 metabolism; peroxisome +++/++ biogenesis D84145 WS-3^(a) dynatin 6 ++ +/++ D14661 KIAA0105^(a)transcription; WT1- ++ +/++ associating protein X77548 NCOA4transcription; nuclear ++ +/++ receptor coactivator M90696 CTSS cysteineprotease ++ +/++ D11086 IL2RG cytokine receptor ++ +/++ U70426 RGS16signaling; GTPase ++ +/+++ activating protein X14850 H2AX histone;required for ++ +/+++ antibody maturation M29927 OAT metabolism;ornithine ++ +/+++ aminotransferase S74017 NFE2L2 transcription; +++/+++ HT4604 GYG metabolism; glycogen ++ +/+++ biogenesis M55531 SLC2A5metabolism; fructose ++ +/++++ transporter M60750 H2BFL histone;nucleosome ++ +/++++ L19437 TALDO1 metabolism; transaldolase ++ ++/+++M10901 NR3C1 transcription; glucocorticoid ++ ++/+++ receptor L41887SFRS7 MRNA splicing factor ++ ++/+++ M34423 GLB1 metabolism;galactosidase ++ ++/++++ X15414 AKR1B1 metabolism; aldose +++ +/++++reductase J04456 LGALS1 signaling; inhibits CD45 +++ +/++++ phosphataseX92493 PIP5K1B signaling; kinase +++ +/++++ U51478 ATP1B3 Na+, K+transporter +++ ++/++++ X91257 SARS seryl-tRNA synthetase +++ ++/++++D30655 EIF4A2 translation initiation +++ ++/++++ D31887 KIAA0062^(a)unknown +++ ++/++++ X04106 CAPN4 cysteine protease; calcium +++ ++/++++dependent D87442 NCSTN^(a) nicastrin +++ ++/++++ L76191 IRAK1 signaling;cytokine receptor +++ +++/++++ kinase HT1428 HBB hemoglobin ++++ −/++++U44975 COPEB oncogene; transcription ++++ −/++++ factor X55733 EIF4Btranslation initiation ++++ +/++++ L09604 PLP2 signaling; colonic +++++/++++ epithelium differentiation HT1614 PPP1CA signaling; phosphatase++++ +++/++++ L26247 SUI1^(a) translation initiation; ++++ +++/++++probable

EXAMPLE 17

[0189] Hierachical Cluster Analysis with EDG-MM, LDG-MM1, and LDG-MM2Reveals Developmental Stage-Based Classification of Multiple Myeloma

[0190] To identify whether variability in gene expression seen inmultiple myeloma (MM) might be used to discern subgroups of disease,hierarchical cluster analysis was performed on 74 newly diagnosed MM, 7tonsil B cell, 7 tonsil plasma cell, and 7 bone marrow samples using theEDG-MM (FIG. 12), LDG-MM1 (FIG. 13), and LDG-MM2 (FIG. 14). Hierarchicalclustering was applied to all samples using 30 of the 50 EDG-MM. A totalof 20 genes were filtered out with Max−Min<2.5. This filtering wasperformed on this group because many of the top 50 EDG-MM showed novariability across MM and thus could not be used to distinguish MMsubgroups. A similar clustering strategy was employed to cluster thesamples using the 50 LDG-MM1 and 50 LDG-MM2.

[0191] The MM samples clustering with the tonsil B cell samples werethen identified to determine whether the MM cases clustering with tonsilB cells, or tonsil and bone marrow plasma cells could be correlated withgene expression-defined MM subgroups (Table 16). This data showed thatof the MM cases clustering tightly with the tonsil B cell samples, 13 of22 were from the MM4 subgroup, accounting for a majority of all MM4cases (13 of 18 MM4 samples). The LDG-MM defined cluster distribution ofgene expression-defined M M subgroups was dramatically different in that14 of the 28 MM samples clustering with the tonsil plasma cell sampleswere from MM3 subgroup (14 of 15 MM3 samples). LDG-MM2 again showed astrong correlation with the MM subgroups in that 14 of the 20 MM casesin this cluster were from the MM2 subgroup (14 of 21 MM2 cases). Thus,the MM4, MM3, and MM2 subtypes of MM have similarities to tonsil Bcells, tonsil plasma cells, and bone marrow plasma cells respectively.MM1 represented the only subgroup with no strong correlations withnormal cell counterparts tested here, suggesting that this class hasunique characteristics yet to be uncovered.

[0192] The distribution of the four MM subgroups in the normal cellcluster groups was determined next (Table 17). The results demonstratethat whereas all MM3 cases were able to be classified, 6 MM1, 5 MM2, and3 MM4 cases were not clustered with any normal cell group in any of thethree cluster analyses. In all samples capable of being clustered, therewere strong correlations between gene expression-defined subgroups andnormal cell types with the exception of MM1. The data also show that 3MM1, 2 MM2, 4 MM3, and 1 MM4 cases were found to cluster in two groups.No samples were found in three groups and all cases clustering with twonormal classes were always in an adjacent, temporally appropriategroups. P241 was an exception in that it was clustered in the bonemarrow plasma cell and tonsil B cell groups.

[0193] Because one of the EDG-MMs was discovered to be cyclin B1 (CCNB1)(Table 13), it was determined if a panel of proliferation associationgenes recently discovered to be up-regulated in MM4 could be used toadvance and validate the classification of MM4 as a so-called tonsil Bcell-like form of MM. Box plots of the expression patterns of CCNB1,CKS1, CKS2, SNRPC, EZH2, KNSL1, PRKDC, and PRIM1 showed significantdifferences across all the groups tested with strong significantcorrelation between tonsil B cells and MM4 (FIG. 15). Several importantobservations were made in this analysis. For all the genes, with theexception of SNRPC, there was a progressive reduction in expression inthe transition from tonsil B cells to tonsil plasma cells to bone marrowplasma cells. In addition, striking correlations were observed withPRIM1 (FIG. 15). Although PRIM1 expression was significantly differentacross the entire group (P=4.25×10⁻⁵), no difference exists betweentonsil B cells and MM4 (Wilcoxon rank sum [WRS] P=0.1), or betweentonsil plasma cells and MM3 (WRS P=0.6). Given the important function ofseveral transcription factors in driving and/or maintaining plasma celldifferentiation, it was determined if these factors showed alteredexpression across the groups under study. Although other factors showedno significant changes, XBP1 (FIG. 15) showed an enormous up-regulationbetween tonsil B cells and tonsil plasma cells as expected. However, thegene showed a reduction in bone marrow plasma cells and a progressiveloss across the four MM subgroups with MM4 showing the lowest level(P=3.85×10⁻¹⁰).

[0194] Based on conventional morphological features, plasma cells havebeen thought to represent a homogeneous end-stage cell type. However,phenotypic analysis and gene expression profiling disclosed hereindemonstrated that plasma cells isolated from distinct organs can berecognized as belonging to distinct stages of development. Multiplemyeloma plasma cells are derived from the bone marrow and are thought torepresent a transformed counterpart of normal terminally differentiatedbone marrow plasma cells. However, the dramatic differences in survival,which can range from several months to greater than 10 years, suggeststhat multiple myeloma may represent a constellation of several subtypesof disease. Conventional laboratory parameters have not been particularuseful in segregating distinct disease subtypes with sufficientrobustness that would allow adequate risk stratification. In addition,unlike achievements in classifying leukemias and lymphomas based onsimilar nonrandom recurrent chromosomal translocations, the extremekaryotypic heterogeneity of multiple myeloma has made attempts atunderstanding the molecular mechanisms of the disease and classificationprediction virtually impossible.

[0195] In studies presented here, it was identified that many EDGs andLDGs exhibit highly variable expression in multiple myeloma, suggestingthat multiple myeloma might be amenable to a developmental stage-basedclassification. It appears from the results of this study that multiplemyeloma can in fact be classified based on similarities in geneexpression with cells representing distinct stages of B celldifferentiation. This developmental based-system in conjunction with thegene expression-based system reported above represents a criticalaffirmation of the validity of the developmental-based system.

[0196] Recent studies provide support for the hypothesis that MM3represents a tonsil plasma cell-like form of the disease. Microarrayprofiling with the U95Av2 GeneChip on 150 newly diagnosed patients(including the 74 described here) along with an analysis of chromosome13 loss has revealed a significant link between reduced RB1 transcriptswith either monosomy or partial deletions of chromosome 13 (unpublisheddata). In these studies, it was observed that a number of multiplemyeloma cases with or without chromosome 13 deletion had RB1 transcriptsat levels comparable to those seen in normal tonsil plasma cells. FISHanalysis with a bacterial artificial chromosome BAC covering RB1demonstrated that these cases did not have interstitial deletions of theRB1 locus. Given that RB1 was found to be a LDG-MM 1, it was determinedif the low levels of RB1 may be linked to tonsil plasma cell-like MM,i.e MM3. Of 35 multiple myeloma cases with RB1 AD values of <1100 (RB1AD value not less than 1100 in 35 normal bone marrow plasma cell samplestested), 74% belonged to the MM3 class. In contrast, of 38 multiplemyeloma cases lacking deletion 13 and having RB1 AD values greater than1100, only 21% belonged to the MM3 subtype (unpublished data).

[0197] Although there is a significant link between the celldevelopment-based classification and gene expression profiling-basedclassification disclosed herein, there are exceptions in that althoughas expected the majority of the MM4 cases belonged to the tonsil Bcell-cluster subgroup, 5 MM3, 1 MM2, and 3 MM1 cases were also found inthis cluster. The recognition that cases within one geneexpression-defined subgroup could be classified in two normal celldefined clusters suggests these cases may have intermediatecharacteristics with distinct clinical outcomes. It is of interest todetermine if the unsupervised gene expression-based system ordevelopmental stage-based system alone or in combination will allow thecreation of robust risk stratification system. This can be tested byallowing sufficient follow-up time on >150 uniformly treated multiplemyeloma cases in which profiling has been performed at diagnosis.

[0198] MM1 was the only gene expression-defined subgroup lacking strongsimilarities to any of the normal cell types analyzed in this study. Itis possible that MM1 has similarities to either mucosal-derived plasmacells or peripheral blood plasma cells which has recently been shown torepresent a distinct type of plasma cells. Future studies will be aimedat providing a developmental stage position for this subtype.

[0199] The hypoproliferative nature of multiple myeloma, with labelingindexes in the clonal plasma cells rarely exceeding 1%, has lead to thehypothesis that multiple myeloma is a tumor arising from a transformedand proliferative precursor cell that differentiates to terminallydifferentiated plasma cells. It has been shown that there is a bonemarrow B cell population transcribing multiple myeloma plasmacell-derived VDJ joined to IgM sequence in IgG- and IgA-secretingmultiple myelomas. Other investigations have shown that the clonogeniccell in multiple myeloma originates from a pre-switched but somaticallymutated B cell that lacks intraclonal variation. This hypothesis issupported by recent use of single-cell and in situ reversetranscriptase-polymerase chain reaction to detect a high frequency ofcirculating B cells that share clonotypic Ig heavy-chain VDJrearrangements with multiple myeloma plasma cells. Studies have alsoimplicated these precursor cells in mediating spread of disease andaffecting patient survival.

[0200] Links of gene expression patterns between subsets of multiplemyeloma and cells representing different late stages of B celldifferentiation further support the above hypothesis in that MM4 and MM3may have origins in a so called “multiple myeloma stem cell”. Thishypothesis can be tested by isolating B cells from tonsils or lymphnodes or peripheral blood of MM3 and MM4 patients, differentiating theminto plasma cells in vitro using a new method described by Tarte et al.(2002) and then testing for the presence of an multiple myeloma geneexpression signature within the differentiated populations. Even if themultiple myeloma stem cell represents a minority population in the Bcells, the multiple myeloma gene expression signature may be recognized,if not with conventional microarray, then by more sensitive quantitativereal-time RT-PCR. A real time RT-PCR method is envisioned as expressionprofile models using at little as 20 genes that distinguish malignantmultiple myeloma plasma cells from normal plasma cells at an accuracy of99.5% have been developed (unpublished data).

[0201] Regardless of the outcome of these experiments, it is clear thatgene expression profiling has become an extremely powerful tool inevaluating the molecular mechanisms of plasma cell differentiation andhow these events relate to multiple myeloma development and progression,which in turn should provide more rational means of treating thiscurrently fatal disease. TABLE 16 Distribution of Multiple MyelomaSubgroups in Hierarchical Clusters Defined by EDG-MM, LDG-MM1, andLDG-MM2 Genes Gene Expression-Defined MM Subgroups Normal Cell- MM1 MM2MM3 MM4 Defined Cluster (n = 20) (n = 21) (n = 15) (n = 18) P EDG-MM 3 15 13 .00005 (n = 22) LDG-MM1 8 4 14 3 .000008 (n = 29) LDG-MM2 6 14 0 0.000001 (n = 20)

[0202] TABLE 17 Distribution of Gene Expression-Defined Multiple MyelomaSubgroup Cases in Normal Cell Clusters defined by EDG-MM, LDG-MM1, andLDG-MM2 MM1 TBC TPC BPC MM2 TBC TPC BPC MM3 TBC TPC BPC MM4 TBC TPC BPCP026 Y Y P237 Y Y P052 Y Y P034 Y Y P037 Y Y P241 Y Y P098 Y Y P051 YP029 Y Y P079 Y P107 Y Y P057 Y P061 Y P083 Y P158 Y Y P063 Y P066 YP121 Y P119 Y P065 Y P006 Y P144 Y P221 Y P075 Y P120 Y P157 Y P030 YP084 Y P131 Y P171 Y P043 Y P122 Y P002 Y P176 Y P053 Y P127 Y P010 YP213 Y P055 Y P154 Y P067 Y P215 Y P138 Y P187 Y P226 Y P251 Y P155 YP199 Y P025 Y P250 Y P163 Y P255 Y P082 Y P222 Y P239 Y P054 Y P085 P103Y P175 Y P101 Y P099 P202 Y P056 P001 P015 P091 P016 P048 P168 P036 P124P118 P212 P249

EXAMPLE 18

[0203] Diagnostic Models that Distinguish Multiple Myeloma, MonoclonalGammopathy of Undetermined Significance, and Normal Plasma Cells

[0204] The molecular mechanisms of the related plasma cell dyscrasiasmonoclonal gammopathy of undetermined significance (MGUS) and multiplemyeloma (MM) are poorly understood. Additionally, the ability todifferentiate these two disorders can be difficult. This has importantclinical implications because MGUS is a benign plasma cell hyperplasiawhereas MM is a uniformly fatal malignancy. Monoclonal gammopathies arecharacterized by the detection of a monoclonal immunoglobulin in theserum or urine and underlying proliferation of a plasma cell/B lymphoidclone. Patients with MGUS have the least advanced disease and arecharacterized by a detectable plasma cell population in the marrow(<10%) and secretion of a monoclonal protein detectable in the serum(<30 g/L), but they lack clinical features of overt malignancy (such aslytic bone lesions, anemia, or hypercalcemia). Patients with overt MMhave increased marrow plasmacytosis (>10%), serum M protein (>30 g/L),and generally present with anemia, lytic bone disease, hypercalcemia, orrenal insufficiency.

[0205] Approximately 2% of all monoclonal gammopathy of undeterminedsignificance cases will convert to overt multiple myeloma per year, butit is virtually impossible to predict which of these cases will convert.A difficulty in the clinical management of multiple myeloma is theextreme heterogeneity in survival, which can range from as little as twomonths to greater than eight years with only 20% of this variabilitybeing accounted for with current clinical laboratory tests. Thus, thereis a great need for more robust methods of classification andstratification of these diseases.

[0206] This example reports on the application of a panel of statisticaland data mining methodologies to classify multiple myeloma (MM),monoclonal gammopathy of undetermined significance (MGUS), and normalplasma cells. Expressions of 12,000 genes in highly purified plasmacells were analyzed on a high density oligonucleotide microarray.Various methodologies applied to global gene expression data identifieda class of genes whose altered expression is capable of discriminatingnormal and malignant plasma cells as well as classifying some MGUS as“like” MM and others as “unlike” MM. The extremely high predictive powerof this small subset of genes, whose products are involved in a varietyof cellular processes, e.g., adhesion and signaling, suggests that theirderegulated expression may not only prove useful in the creation ofmolecular diagnostics, but may also provide important insight into themechanisms of MM development and/or conversion from the benign conditionof MGUS to the overly malignant and uniformly fatal MM.

[0207] Six different methodologies were employed herein: logisticregression, decision trees, support vector machines (SVM), Ensemble ofVoters with 20 best information gain genes (EOV), naïve Bayes, andBayesian networks. All six models were run on microarray data derivedfrom Affymetrix (version 5) high density oligonucleotide microarrayanalysis. One hundred fifty six untreated MM samples, 34 healthysamples, and 32 samples designated as MGUS were compared. Thenormalization algorithm available from the Affymetrix software was used.Information on normalization and standardization of the microarray datais available on Affymetrix's website.

[0208] Statistical and Data Mining Methodologies

[0209] Various methods were employed with two goals in mind. The firstgoal is to identify genes whose over or under expression are apparent inthe comparison of healthy samples, MGUS (monoclonal gammopathy ofundetermined significance) samples, and malignant MM (multiple myeloma)samples. The second goal is to identify optimal methods for use inanalyzing microarray data and specifically methods applicable toanalyzing microarray data on samples from MGUS and MM patients. Previouswork has been done in identifying lists of genes that discriminatebetween the two types of samples (Zhan et. al., 2002a; Chauhan et. al.,2002), but, to our knowledge, this is the first work that has been doneon simultaneously identifying discriminatory genes and creating modelsto predict and describe the differences between myeloma, MGUS, andhealthy samples.

[0210] For each of the methods (and each of the comparisons), a 10-foldcross validation was employed to estimate the prediction error. Using10-fold cross validation, {fraction (1/10)}^(th) of the data was removed(the ‘test’ data), and the entire model was created using only theremaining 90% of the data (the ‘training’ data.) The test data were thenrun through the training model and any misclassifications were noted.Error rates were computed by compiling the misclassifications from eachof the 10 independent runs. Empirical results suggest that 10-fold crossvalidation may provide better accuracy estimates than the more commonleave one out cross validation (Kohavi, 1995).

[0211] Logistic Regression

[0212] The logistic procedure creates a linear model that yields anumber between zero and one. This value represents a predictiveprobability, for example, of being in the multiple myeloma sample(predictive value close to one) or of being in the normal sample(predictive value close to zero). The structure allows for knowledge ofthe uncertainty in predicting the group membership of future samples.For example, a new sample might be classified with a predictiveprobability of 0.53 and classified as multiple myeloma, albeit with muchless confidence than another sample whose predictive probability is0.99.

[0213] Decision Trees

[0214] Decision tree induction algorithms begin by finding the singlefeature that is most correlated with class. For the present discussion,mutual information was used and the classes were multiple myeloma vs.normal, multiple myeloma vs. MGUS and MGUS vs. normal. For each feature,the algorithm computes the information gain of the detection and of theoptimal split point for the real-valued measure (signal). Informationgain is defined as follows: the entropy of a data set is—plog₂p—(1−p)log₂(1−p) where p is the fraction of samples that are of acertain class. A split takes one data set and divides it into two datasets: the set of data points for which the feature has a value below thesplit point (or a particular nominal value) and the set of data pointsfor which the gene has a value above the split point (or any othernominal value).

[0215] Ensembles

[0216] Even with pruning, decision trees can sometimes over fit thedata. One approach to avoid over fitting is to learn the n best simpledecision trees, and let these trees vote on each new case to bepredicted. The simplest decision tree is a decision stump, a decisiontree with a single internal node, or decision node. Our “Ensemble ofVoters” (EOV) approach is an unweighted majority vote of the top 20decision stumps, scored by information gain.

[0217] Naïve Bayes

[0218] Naïve Bayes is so named because it makes the (often) naïveassumption that all features (e.g. gene expression levels) areconditionally independent of the given class value (e.g. MM or normal).In spite of this naive assumption, in practice it often works very well.Like logistic regression, naïve Bayes returns a probability distributionover the class values. The model simply takes the form of Bayes' rulewith the naïve conditional independence assumption.

[0219] Bayesian Networks

[0220] Bayesian networks (Bayes nets) are a very different form ofgraphical model from decision trees. Like decision trees, the nodes in aBayes net correspond to features, or variables. For classificationtasks, one node also corresponds to the class variable. A Bayes net is adirected acyclic graph (DAG) that specifies a joint probabilitydistribution over its variables. Arcs between nodes specify dependenciesamong variables, while the absence of arcs can be used to inferconditional independencies. By capturing conditional independence whereit exists, a Bayes net can provide a much more compact representation ofthe joint distribution than a full joint table or other representation.There is much current research into the development of algorithms toconstruct Bayes net models from data (Friedman et al., 1999; Murphy,2001; Pe'er et al., 2001.) Bayes nets are proven to be outstanding toolsfor classification. For example, in KDD Cup 2001, an international datamining competition with over 100 entries, the Bayes net learningalgorithm PowerPredictor was the top performer on a data set with strongsimilarities to microarray data (Cheng et al., 2000). This is thealgorithm employed in the present study.

[0221] Support Vector Machines

[0222] Support vector machines (SVMs) (Vapnik, 1998; Cristianini andShawe-Taylor, 2000) are another novel data mining approach that hasproven to be well suited to gene expression microarray data (Brown etal., 1999; Furey et al., 2000.) At its simplest level, a support vectormachine is an algorithm that attempts to find a linear separator betweenthe data points of two classes. SVMs seek to maximize the margin, orseparation between the two classes. Maximizing the margin can be viewedas an optimization task that can be solved with linear programmingtechniques. Support vector machines based on “kernel methods” canefficiently identify separators that belong to other functional classes.A commonly used kernel is the Gaussian kernel. Nevertheless, for geneexpression microarray data, it has been repeatedly demonstratedempirically that simple linear SVMs give better performance (Brown etal., 1999; Furey et al., 2000) than SVMs with other kernels.

[0223] Results

[0224] As mentioned, each model was tested using 10-fold crossvalidation to obtain error (misclassification) rates. For each of 10runs of the data, 10% of the sample was removed and the prediction modelwas created. Then, using the created model, the test sample waspredicted into groups and the accuracy was recorded. After completingall 10 runs, the accuracy values were accumulated into the followingtable (Table 18). TABLE 18 Ten-Fold Cross Validation Results % correctlyclassified MM Normal MM MGUS MGUS Normal Logistic 98.72% 91.18%  89.1% 18.8% 90.63% 97.06% Trees 97.44% 94.12% 87.18%  37.5% 90.63% 94.12% SVM98.72% 97.06% 89.10% 34.38% 90.63%   100% Bayes Net 98.72%   100% 93.56%34.38% 90.63% 97.06% EOV 98.08%   100% 57.69% 68.75% 90.63%   100% NaïveBayes 98.08%   100% 91.67% 43.75% 90.63%   100%

[0225] There does not appear to be one methodology that stands out fromthe rest in terms of predicting group membership. In the difficultclassification of multiple myeloma (MM) vs. MGUS, Ensemble of Votersclassifies the most MGUS correctly (68.75%), but the fewest multiplemyeloma correctly (57.69%.) Using naïve Bayes produces the bestclassification, though it does not seem to be appreciably better thanthe other methods. All the methods appear to be able to classifymultiple myeloma vs. Normal quite well and MGUS vs. Normal almost aswell.

[0226] To test the difference of accuracy across procedures, a pairedt-test was done for the overall correct classification rate for each ofthe comparisons on each of the folds of the procedures. None of themethods were significantly different (p≦0.05) except the EOV whencompared to the other methods in the MGUS vs. multiple myeloma test. Thepaired t-tests give p-values between 0.002 and 0.031 (unadjusted formultiple comparisons) for the EOV compared with the other 5 models inthe MGUS vs. multiple myeloma test. According to this test, the EOV hasa significantly lower rate of correct classification, though it is themost accurate MGUS classifier as shown above. In comparing two groups,this is often the trade off between sensitivity and specificity.

[0227] Models for predicting group membership were identified for eachmethod. The models classifying multiple myeloma vs. MGUS had moreoverlapping genes (17 unique genes) then the models classifying multiplemyeloma vs. Normal (12 unique genes) or MGUS vs. Normal (10 uniquegenes.) A possible explanation for this is that there are probablynumerous genes that distinguish multiple myeloma and normal samplesbecause the two groups are quite distinct. However, the geneticsimilarities between multiple myeloma and MGUS lead to fewer number ofgenes that are different across the two groups. This dearth ofdistinguishing genes conditions any good model to contain some of thesame limited number of genes. A more detailed discussion of theparticular genes is given in the conclusion.

[0228] Meta-Voting

[0229] As an additional step to improve the prediction capabilities ofthe method, a “meta” prediction value was calculated. For each of thelogistic regression, SVM, and Bayes Net procedures, the marginalpredicted group was calculated and then a final prediction was given asthe top voted group. A sample is classified in a group if at least twoof the three methods predict that group. The calculation indicate thatthe meta voting procedure does not improve the results.

[0230] Receiver Operator Characteristic (ROC) Curves

[0231] A Receiver Operating Characteristic (ROC) curve demonstrates therelationship between sensitivity (correct prediction to the morediseased group) and specificity (correct prediction to the less diseasedgroup). FIG. 16 gives the ROC curves for the comparison of MM (multiplemyeloma) vs. MGUS classification. The difficult comparison (multiplemyeloma vs. MGUS) is challenging for all the methods. For example, naïveBayes has a high sensitivity but at the cost of low specificity. Foreven mediocre values of specificity, the sensitivity drops off quiterapidly. In order to have a high sensitivity for any of the methods(that is, in order to have very few false positives of multiplemyeloma), the ability to predict MGUS accurately (specificity) wascompromised.

[0232] Prediction of MGUS

[0233] The models that classify the multiple myeloma and normal samplesinto distinct groups may also be able to be used as a predictive modelfor samples that are not clearly in either group based on clinical data.As a whole, the MGUS samples are clinically healthy (except for highlevels of immunoglobulins) but genetically appear malignant. Applyingthe multiple myeloma vs. normal model to the MGUS samples will give usan idea as to which group the MGUS samples look more like. Table 19provides the prediction distribution for the MGUS samples into themultiple myeloma and normal groups based on the model which comparedmultiple myeloma to normal samples. On average, about 90% of the MGUSsamples are classified as multiple myeloma, and about 10% are classifiedas normal. The possible reason for this is that the 10% who areclassified as normal may have longer survival times and less diseaseprogression. Regardless, the similarity of MGUS to multiple myeloma(even in the model that was derived without any MGUS) gives additionalevidence that the MGUS is actually genetically much more similar to themultiple myeloma than to the normal samples. From both the prediction ofthe dichotomous groups and the classification of MGUS samples into thetwo extreme groups, it can be concluded that the methods are not notablydifferent.

[0234] In order to better understand the mechanisms behind the poorclassification of the MGUS samples (when compared to multiple myeloma),the number of MGUS classified as multiple myeloma for each of threemethods, logistic regression, SVM, and Bayes Net was tabulated. Of the32 MGUS samples, the misclassification rates are given in Table 20.There were 26 MGUS samples misclassified using the logistic procedure;17 of the 26 were also misclassified using SVM, and 18 of the 26 weremisclassified using Bayes Net. This cross tabulation indicates that themisclassified MGUS samples are continuously getting misclassified whichlends evidence to a possible subset of MGUS samples that are geneticallysimilar to the multiple myeloma samples. TABLE 19 MM vs. Normal(predicting MGUS) % MGUS classified as: MM Normal Logistic  87.5% 12.5%Trees 93.75% 6.25% SVM 93.75% 6.25% Bayes Net 93.75% 6.25% EOV 84.37%15.63%  Naïve Bayes 93.75% 6.25%

[0235] TABLE 20 # MGUS misclassified Logistic SVM Bayes Net Logistic 2617 18 SVM 21 17 Bayes Net 21

[0236] Discussion

[0237] Six different statistical and data mining algorithms wereexamined for their ability to discriminate normal, hyperplastic, andmalignant cells based on the expression patterns of ˜12,000 genes. Themodels were highly accurate in distinguishing normal plasma cells fromabnormal cells. However, these models displayed a uniform failure in thediscrimination between the hyperplasic cells and malignant cells. Amajor goal of this study was to develop or modify data mining tools inorder to capture a small subset of genes from massive gene expressiondata sets to accurately distinguish groups of cells, e.g. normal,precancerous, and cancerous cells, with the ultimate goal to createsensitive and reproducible molecular-based diagnostic tests. Inaddition, future studies can be aimed at using a similar strategy toidentify a minimum subset of genes capable of discriminating subgroupsof disease for risk stratification and prognostics. This is aparticularly important concept for this disease as the overall survivalin multiple myeloma is highly variable, with some patients surviving aslong as 10 years while others die within several months of diagnosis.Current microarrray studies require the isolation of large numbers ofcells that necessitate advanced facilities and expertise. The studiesdescribed in this example represent the first step toward streamliningthis process, as a smaller subset of genes (10-20) with a highpredictive power allows for a massive reduction in scale, which in turnwill make development of a commercial test more amenable to massproduction and hence widespread clinical use.

[0238] One possible reason for the inability of the models todiscriminate MGUS (monoclonal gammopathy of undetermined significance)from multiple myeloma is that MGUS represents at least two differentdiseases. This is supported by the overlap in misclassification of MGUSsamples as shown in Tables 19-20. In simplistic terms, MGUS can beviewed as a disease that will remain indolent or one that will convertto overt malignancy. Accruing sufficient numbers of stable andprogressive MGUS cases along with sufficient follow-up time will helpresolve this issue.

[0239] The failure of the models to differentiate the two disease typescould be related to the limitations of the current methodologies. Themicroarray profiling utilized here only interrogated ⅓ of the estimated35,000 human genes (International Human Genome Sequencing Consortium,2001; Venter et al., 2001), thus it is possible that a whole genomesurvey would reveal discriminating features. A new Affymetrix U133GeneChip system which is thought to interrogate all human genes may beused to address this question. It is also possible that a whole genomeanalysis will reveal no significant differences. This revelation couldmean any of a variety of possibilities: (1) there is no geneticdifference between the two diseases, (2) only the MGUS that areclassified as multiple myeloma are genetically similar to multiplemyeloma, and the clinical tests are unable to identify that distinction,(3) the current microarray technology is not specific enough to measurethe differences between the two diseases, (4) the methods describedabove are not appropriate for this type of analysis. If (1) or (2) istrue, these results would point to other determinants of an indolent ormalignant course such as genetic predisposition or somatic DNA mutationsnot manifest in gene expression, a unique environmental exposureinteracting with these predisposing genetic traits, or a non-tumor cellmicroenvironment or “soil” that promotes plasma cell growth.

[0240] Another goal of this work was to use the models of global geneexpression profiling to define critical genetic alterations thataccompany the transition of a plasma cell from its normal homeostasis toa benign hyperplasia and from hyperplasia to an overt malignancy.Integration of the data from the six models revealed a group of genesthat were found in two or more of the models. For purposes of this studythese genes were interpreted to represent the most differentiallyexpressed in these transitions. Ten common genes were identified in thenormal to MGUS (monoclonal gammopathy of undetermined significance)comparison with 8 of the genes being down-regulated or shut down in theabnormal cells. A similar phenomenon was seen in the normal versusmultiple myeloma comparison with 9 of 12 common genes beingdown-regulated. This was in contrast to the MGUS versus multiple myelomacomparison where almost half (8 of 18 probe sets representing 17 uniquegenes) of the probe sets were up-regulated in multiple myeloma. Probessets for 4 different chemokine genes SCYA23 (Normal vs. MGUS), SDF1(Normal vs. MM), and SCYC2 and SCYA18 (MGUS vs. MM) were down-regulatedin the latter group in each of the 3 comparisons. Two probe sets forSCYA18 were found in the MGUS vs. MM comparison. This is an importantvalidation of SCYA18 gene expression truly being different in the twoconditions. Chemokines are important mediators of immune responses andact as soluble factors that induce the migration of specific immunecells to sites of inflammation. The potential significance of the lossof expression of multiple chemokine genes in plasma cell dyscrasias isnot understood, but may point to how tumors may suppress anti-tumorimmune reactions.

[0241] As with SCYA18, two unrelated probe sets for the human homologueof the Drospohila melangaster gene frizzled (FZD2) were down-regulatedin the normal to MGUS transition. FZD2 codes for a membrane boundreceptor that binds a highly conserved family of soluble ligands knownas WNTs. WNT signaling regulates homeotic patterning and cell-fatedecisions in multicellular organisms ranging from flies to humans. TheWnt signaling cascade has also been shown to be involved in neoplasia ashyperactivation of the Wnt-1 gene by viral insertional mutagenesiscaused spontaneous mammary tumorigenesis in mice. It is suspected thatloss of FZD2 expression in MGUS carries potential significance giventhat expression profiling has revealed deregulated expression ofmultiple members of the WNT signaling pathway in multiple myeloma andplasma cell leukemia (results shown above; Zhan et al., 2002a; De Vos etal., 2001). Results in previous examples presented above also show thata secreted antagonist of WNT signaling, FRZB, exhibits elevatedexpression in a comparison of normal plasma cells and multiple myeloma(Zhan et al., 2002a; De Vos et al., 2001). The concomitant, or possiblysequential, down-regulation of the functional WNT receptor (FZD2) andup-regulation of a decoy receptor strongly suggests that disruption ofWVNT signaling plays a pathological role in multiple myelomadevelopment. In addition to abnormalities in the receptor and decoygenes, the genes for the ligands, WNT5A and WNT10B, have been identifiedas altered in multiple myeloma (results shown above; Zhan et. al.,2002). Whereas WNT5A is upregulated in multiple myeloma, WNT10B isexpressed at high levels in normal plasma cells but not in a majority ofmultiple myeloma plasma cells (Zhan et. al., 2002a). It is of note thatrecent studies have demonstrated that Wnt-5A, Wnt-2B, Wnt-10B, Wnt-11comprise a novel class of hematopoietic cell regulators.

[0242] Taken together these findings suggests that deregulated autocrineand/or paracrine Wnt signaling may play a pivitol role in plasma celldyscrasias and that a progressive deregulation of multiple components ofthe signaling complex may be associated with disease progression fromnormal plasma cells to hyperplastic, but benign, MGUS then to overtmultiple myeloma. In conclusion, it is anticipated that strategies likethose employed here will allow the creation of new molecular diagnosticand prognostic tests and should provide useful insight into the geneticmechanisms of neoplastic transformation.

EXAMPLE 19

[0243] Elevated Expression of Wnt Signaling Antagonists DKK1 and FRZB inMyeloma Plasma Cells is Associated with Lytic Bone Disease

[0244] Multiple myeloma is the only hematological malignancyconsistently associated with debilitating lytic bone disease. Theprevalence of bone disease varies with the presentation of multiplemyeloma, from smoldering multiple myeloma often without boneinvolvement, to solitary plasmacytoma, to diffuse or focal multiplemyeloma where systemic losses of bone mineral density or focal lyticbone lesions are seen in 80% of patients. Progression from thepre-malignant monoclonal gammopathy of undetermined significance (MGUS)to overt multiple myeloma is preceded by changes in bone turnover rates.Bone loss tends to be located adjacent to malignant plasma cells,suggesting that multiple myeloma plasma cells secrete factors thatinduce osteoclast activation and/or suppress osteoblast growth anddifferentiation.

[0245] To identify secreted factors linked to multiple myeloma bonedisease, the expression profiles of ˜12,000 genes was compared inCD138-enriched plasma cells (PC) from newly diagnosed multiple myeloma(MM) with no radiological evidence of osteolytic bone lesions (n=87) tothose with ≧3 lytic lesions (n=83). Of a total of 367 genes identified,229 were higher and 138 lower in PC from MM with lytic lesions.Expression of genes associated with cell proliferation, e.g. PCNA, TYMS,PRKDC, CENPA, and TOP2A predominated in M M with lytic lesions. Incontrast ARHE, IL-6R, WNT10B, and the B-cell receptor molecules SLAM,TACI, and LNHR, were notable genes expressed at significantly lowerlevels in M M with lytic lesions. Consistent with a critical role of WNTsignaling in osteoblast growth and differentiation (see below), the twosecreted WNT signaling antagonists, soluble frizzled related protein 3(SFRP-3/FRZB) and DKK1, represented the only genes coding for secretedfactors within the top 50 up-regulated genes. Importantly, DKK1 and FRZBwere not expressed in PC from 45 normal bone marrow donors or 10Waldenstrom's macroglobulinemia, a PC malignancy that lacks bonedisease, and monoclonal gammopathy of undetermined significance (MGUS).Both immuno-histochemistry and immunofluoresence of biopsy materialconfirmed gene expression data.

[0246] Since lytic bone lesions develop at sites of MRI-definedmedullary plasmacytoma (MPCT), MRI represents a highly sensitivesurrogate for present or future osteolytic lesions. As MRI-defined MPCTcan be observed in the absence of x-ray detectable lytic lesions, it ishypothesized that those cases with no lytic lesions, yet havingunderlying MPCT might skew the data analysis when correlating geneexpression with x-ray data alone. Thus, the inventors combined x-ray andMRI data and applied χ², WRS, and SAM analyses to compare 65 newlydiagnosed cases of MM exhibiting three or more lytic lesions and threeor more MPCT with 43 cases exhibiting no lytic lesions or MPCT. A totalof 107 genes differentiating the two groups at P<0.001 were identified.Importantly many of the same genes, e.g. cell cycle genes, identifiedabove were also identified in the latter analysis and the levels ofdifference have increased. For example, whereas the ratio (bonedisease/no bone disease) of the mean expression level for DKK1 in thefirst comparison was 2.45, the mean value increased to 6.25 in thelatter. This reflected the fact that virtually all cases with no lyticlesions and moderate to high DKK1 had MRI-defined focal lesions. Themean expression level of DKK1 in the no lytic lesion group was 1674(range 40 to 10828) whereas the mean DKK1 level in the no lytic lesion &no focal lesion group dropped to 625 (range 57 to 4183).

[0247] It is important to note that DKK1 and FRZB expression, asdetermined from bone marrow aspirates of the iliac crest, although verypowerful, can not account for the presence of bone lesions in allpatients, as cases exists that have x-ray lesions in which the multiplemyeloma plasma cells do not express appreciable levels of DKK1 or FRZB.Several possibilities may account for this observation. First, it ispossible that if plasma cells were isolated from MPCT at focal lesionsthe cells might have high levels of either these genes. Alternatively, aquantitative trait locus (QTL) for low bone mass in the generalpopulation may enhance bone lesion development even in the presence oflow levels of DKK1 and FRZB. Finally, it is likely that DKK1 independentmechanisms, e.g. osteoclast hyperactivation, are also at work in thedevelopment of bone disease. Nonetheless, it is remarkable that cellsfrom random bone marrow aspirates of the iliac crest have geneexpression profiling (GEP) features expected of plasma cells derivedfrom MPCT adjacent to lytic lesions. Thus comparative GEP studies ofCT-guided fine needle aspirates of MPCT and random aspirates from thesame patient should reveal stronger similarities rather thandifferences. Preliminary studies of this design have in fact revealed nomajor distinguishing GEP features, including of DKK1 and FRZB, betweenthe two cell types.

[0248] It have recently shown that expression of the cell cycle controland DNA metabolism genes TYMS, UBE2C, CCNB1, PCNA, TK1, BUB1, BUB1,EZH2, and TOP2A is significantly higher in MM with metaphase cytogeneticabnormalities (CA) and that these features are linked to poor survival.These same genes were also over-expressed in MM with lytic lesions andMPCT and especially in cases with both, suggesting this type of MM isalso likely to have a high proliferation index and may provide amolecular explanation for why MM with >3 lytic lesions is classified asa high risk stage III disease in the Durie Salmon staging system.

[0249] Although exhibiting highly variable and sometimes very highexpression in multiple myeloma plasma cells, MIP-1-alpha (CCL3/SCYA3), achemokine implicated in OCL development and multiple myeloma bonedisease, was not significantly differentially expressed in thisanalysis. In addition, RANKL, a known osteoclast differentiation factorwith conflicting data concerning its expression on multiple myelomaplasma cells, has not been detected in any multiple myeloma plasma cellsor normal bone marrow plasma cell sample tested with our microarraysystem.

[0250] Consistent with a key role for JUN in controlling DKK1 expressionand in turn apoptosis, it has been shown that plasma cells derived fromextramedullary disease, as well as primary refractory disease, and humanmultiple myeloma cell lines have low to undetectable levels of both JUNand DKK1. It has also been shown that primary multiple myeloma cellsco-cultured with in vitro derived osteoclasts are long-lived andmoderately proliferative and that these cells down-regulate JUN, FOS,FOSB, and DKK1 after co-culture (Yaccoby et al., 2002). Thus, one of themechanisms by which OCL may prolong multiple myeloma plasma cellsurvival is through the down-regulation of DKK1 via the down-regulationof JUN.

[0251] The relevance of elevated DKK1 and FRZB expression in multiplemyeloma bone disease is derived from several recent studies that haveshown that functional Wnt signaling is critical for osteoblastdifferentiation and function. Patients exhibiting loss of functionmutations in the low-density lipoprotein receptor-related protein 5(LRP5), a co-receptor for the Wnt ligand, have a condition known asosteoporosis-pseudoglioma (OPPG). Importantly, separate and distinctmutations in LRP5 result in a high bone mass (HBM) phenotype. Incontrast to the OPPG mutations, the HBM defects representgain-of-function mutations that effectively block binding of theinhibitory protein DKK1. Thus, it is speculated that high local levelsof secreted DKK1 and FRZB at the sites of MPCT blocks LRP5 function onosteoblasts, which in turn results in apoptosis or a block to terminaldifferentiation.

[0252] Obligate carriers of the OPPG mutations can have reduced bonemass and a QTL near LRP5 on 11q12 influences bone density in the generalpopulation. It is therefore conceivable that multiple myeloma bonedisease may also be influenced by this QTL, as patients inheriting a lowbone mass allele and whose multiple myeloma plasma cells express highlevels of DKK1 may be at higher risk of developing bone disease thanthose with just one of these features. The inheritance patterns of thisQTL could also help explain the presence of lytic lesions in patientswith low DKK1 and low FRZB. It is also interesting to speculate that therisk of conversion of monoclonal gammopathy of undetermined significance(MGUS) to multiple myeloma may also be influenced by this QTL. Thus itis of interest to determine the genotypes for this locus in a largepopulation of multiple myeloma and MGUS cases and age-gender matchedcontrols and correlating this with DKK1 expression, bone mass, and bonedisease.

[0253] Osteolysis is the most common skeletal manifestation of neoplasiaand may be focal or generalized. Given that prostate and placenta arethe only adult tissues that express appreciable levels of DKK1 and thatmetastatic prostate adenocarcinomas to the bone are frequent andassociated with osteoblastic and sometimes osteolytic lesions, it isintriguing to speculate that metastatic prostate cancers as well asother bone metastasizing tumors expressing high levels of DKK1 may alsocontribute to osteolytic lesions. It is also interesting to speculatethat osteoporosis in the general population may also be linked toelevated DKK1.

[0254] The data linking DKK1 up-regulation after drug treatment, itsdown-regulation in late stage disease and after OCL co-culture, its highexpression in multiple myeloma with bone lesions, and published reportson the molecular biology of DKK1 expression has prompted the inventorsto develop a working model for DKK1 in multiple myeloma. It isenvisioned that in the early stages of disease, an inherent genomicinstability induces expression of DKK1 which in turn induces apoptosisof multiple myeloma cells and may explain the relatively slowprogression of the disease, as cell growth is tempered by a high rate ofDKK1 induced apoptosis. However, as the disease progresses, high levelsof DKK1 secreted from the multiple myeloma cells results in profounddefects in osteoblast function and combined with osteoclasthyper-activation leads to uncoupled bone turnover and the developmentlytic bone lesions and diffuse osteopenia. As osteoclast numbersincrease these cells induce the down-regulation of JUN and DKK1 inmultiple myeloma plasma cells, liberating the multiple myeloma plasmacells from the pro-apoptotic effects of DKK1, resulting in an increasedtumor cell burden. Although DKK1 loss in the early stages of diseaseprogression is likely due to down-regulation of JUN that is induced byan osteoclast derived factor, permanent loss of DKK1 expression late indisease is likely due to the loss of p53. Thus, DKK1 in multiple myelomahas a paradoxical twist, in that even though secreted DKK1 mayfacilitate apoptosis of multiple myeloma plasma cells, it may also blockosteoblast growth and differentiation, which in turn creates a bonemarrow microenvironment primed for disease progression. If true,utilization of this knowledge could lead to the development of newtherapies for MM that accentuate and preserve the pro-apoptotic effectsof DKK1 on MM PC, but at the same time prevent its bone damagingeffects.

EXAMPLE 20

[0255] DKK1 is Secreted by Plasma Cells and Contributes to OsteolyticLesions in Multiple Myeloma

[0256] Examples 20-22 show that DKK1 is the only gene coding forsecreted proteins that is significantly elevated in the malignant plasmacells from patients with bone lesions at diagnosis. DKK1 protein issynthesized and secreted into the marrow space where elevated levelsalso correlate with plasma cell gene expression levels and the presenceof bone lesions. Finally, it is shown that plasma from multiple myelomabone marrow containing high levels of DKK1 blocks in vitro osteoblastdifferentiation in a DKK1-dependent manner, whereas plasma from normalhealthy donors does not.

[0257] Because osteolytic lesions develop at the site of MRI-definedfocal medullary plasmacytomas and DKK1 levels strongly correlate withthe presence of MRI-focal lesions, it is speculated that DKK1 proteinlevels are high in the environment surrounding “nests” of plasma cells,leading to an incapacitation of osteoblasts adjacent to these plasmacell clusters. Concentration gradients of the Wnt ligands and theirsoluble inhibitors, e.g. DKK1, are important in the creation of bodysegmentation and cellular polarity. Thus, DKK1 may exert most of itsinhibitory effects directly adjacent to plasmacytomas, as thus mayprovide a functional explanation as to why lytic lesions are alwaysfound next to medullary plasmacytomas and not in unaffected areas.Because plasma cells make up ≦5% of all bone marrow cells and plasmacells are the only source of DKK1 in human bone marrow, elevated DKK1levels in the marrow could also be related to systemic osteoblastdefects and osteopenia when tumor cell burden reaches a criticalthreshold. Published work showed that when tumor cell burden increasesabove 50%, osteoblast mineral apposition rates are dramatically reduced.

[0258] Not all multiple myeloma patients with bone lesions exhibitedelevated levels of DKK1. Several possibilities may account for thisobservation. Obligate carriers of the OPPG mutations can have reducedbone mass and a quantitative trait loci (QTL) near LRP5 on 11q12influences bone density in the general population. Thus, QTL inheritancepatterns could explain the presence of lytic lesions in patients withlow DKK1 expression, as the presence of a low bone mass allele and lowDKK1 levels may create a high risk for developing bone lesions. It isalso possible that the Wnt signaling antagonist FRZB, which is elevatedin multiple myeloma and multiple myeloma with osteolytic lesions (datanot shown), and which negatively regulates chondrocyte development couldsynergize with DKK1 to contribute to osteoblast dysfunction and bonelesions.

[0259] Although Wnt/β-catenin signaling defects are implicated inhereditary syndromes affecting bone, the data presented herein are thefirst evidence that disrupting this signaling cascade is also linked tobone destruction in non-hereditary conditions. These data also supportthe notion that elevated DKK1, and possibly other Wnt signalingantagonists, might play a role in other forms of bone loss. Soluble Wntantagonist SFRP-2, and possibly DKK1, is downregulated by estrogen; thusit is important to determine whether DKK1 protein levels or other Wntantagonists are elevated in post-menopausal women and whether proteinlevels correlate with osteoporosis. Furthermore, given that breastcancer metastasis to the bone is frequently associated with osteolyticlesions, it is important to determine if DKK1 expression is elevated inthese cases. Finally, these data support the concept thatpharmacological antagonism of DKK1 action may be a means of controllingboth malignancy-related bone loss, and post-menopausal or other forms ofosteoporosis.

[0260] Global Gene Expression Profiling of Malignant Plasma Cells LinksDKK1 with Bone Lesions

[0261] One hundred seventy four patients with newly diagnosed multiplemyeloma, 45 normal healthy donors, and 10 patients with Waldenstrom'smacroglobulinemia were studied in this example. The characteristics ofthe multiple myeloma patients are presented in Table 21.

[0262] Bone images were reviewed on a Canon PACS (Picture Archiving andCataloging System). MRI scans were performed on 1.5 Tesla GE Signa®scanners. X-rays were digitized from film in accordance with AmericanCollege of Radiology (ACR) standards. MRI scans and x-rays were linkedto the Canon PACS system using the ACR's DICOM (Digital Imaging andCommunications in Medicine) standard. Imaging was done in accordancewith manufacturers' specifications. MRI images were created with pre-and post-gadolinium T1-weighting and STIR (short-tau inversion recovery)weighting.

[0263] MRI-defined focal lesions can be observed before the developmentof radiologically identifiable lytic lesions; therefore, T1-weighted andSTIR-weighted imaging was used to evaluate bone lesions in multiplemyeloma. Bone disease in multiple myeloma patients was modeled usinglogistic regression. DKK1 protein and gene expression values, measuredusing the signal calls from the Version 5.01 Affymetrix Analysis Suite,were transformed using the Log Base 2 function prior to entry into thelogistic regression model. Candidate genes were reduced to 58 genes withthe lowest p-value from logistic regression (P<0.00012). T-tests withpermutation-adjusted p-values verified the significance of the 58 genesat the 0.05 level. Except in the process of reducing candidate genes,p-values less than 0.05 were considered significant and p-values lessthan 0.10 were considered marginally significant. Expression intensitiesof genes identified by logistic regression were visualized withClusterView (Li and Wong, 2001). Gene expression and protein levels werecorrelated with Spearman or Pearsons correlation metric.

[0264] Logistic regression and permutation analysis of global geneexpression differences in purified plasma cells from patients with none(n=36) and those with 1 or more (1+) (n=137) MRI-defined focal lesionsidentified 30 downregulated and 28 upregulated genes that weresignificantly differentially expressed (P<0.0001) in patients with 1+MRI lesions (FIG. 17A). The soluble Wnt/β-catenin signaling antagonistDKK1 ranked 4th in significance (P=4.3×10⁻⁶) and represented the onlygene coding for a secreted factor in the list. DKK1 was undetectable bymicroarray analysis of bone marrow plasma cells from 45 normal healthydonors and 10 patients with Waldenström's macroglobulinemia, a plasmacell malignancy that lacks lytic bone disease (data not shown).

[0265] Using the same comparative ranking criteria for x-rayidentifiable osteolytic lesions, DKK1 expression was again significantlyelevated in patients with 1+ lesions (P=0.0068), but ranked 199 of˜10,000 genes investigated (data not shown). Because osteolytic lesionsalways develop at sites of MRI-detectable focal lesions and MRI lesionscan be present in the absence of lytic lesions, it is hypothesized thatmultiple myeloma patients with no x-ray lesions, but high DKK1expression, would have MRI lesions, thus explaining the reduced rankingof DKK1. Indeed, removing patients with 1+ MRI lesions from the groupwith no x-ray lesions resulted in DKK1 moving to the 5th mostsignificant gene (P=0.0000089) when comparing those patients with nox-ray lesions and no MRI lesions with patients who had 1+ x-ray lesions(data not shown).

[0266] Increased Log2 (DKK1) expression increases the likelihood ofhaving 1+ MRI lesions (OR 1.42 [95% CI: 1.21, 1.66], P<0.0001) (FIG.17B). This relationship is still statistically significant, but of lessmagnitude for patients with 1+x-ray lesions (OR: 1.19 [95% CI: 1.04,1.35], P=0.0084). Again, removing patients with 1+ MRI lesions from theno x-ray lesion group reveals that the relationship of DKK1 and lesionsdetected by x-ray (adjusted) (OR 1.43 [95% CI: 1.20, 1.69], P<0.0001)mirrors that of lesions detected by MRI. Though this is not conclusiveevidence of a link between elevated DKK1 and bone lesions, the patternof DKK 1 expression is consistent with the theory that DKK1 and MRI arestrong early indicators of x-ray-defined osteolytic lesions.

[0267] Immunohistochemistry and Immunofluorescence

[0268] A polyclonal goat anti-human DKK1 antibody (R&D Systems,Minneapolis, Minn.) diluted 1:200 in TBS was incubated onformalin-fixed, paraffin-embedded multiple myeloma bone marrow biopsysections for 2 hours at room temperature. Anti-kappa or -lambdaantibodies (Dako, Carpenteria, Calif.) diluted 1:1200 were incubated onadjacent sections for 30 minutes at room temperature. Antigen-antibodyreactions were developed with DAB (using biotinylated anti-goat antibody(1:400 dilution, Vector Laboratories, Burlingame, Calif.) andstreptavidin-horse radish peroxidase (Dako)), and counterstained withHematoxylin-2. For immunofluorescence, cells were fixed in 4%paraformaldehyde, blocked with 1% bovine serum albumin (BSA)/Hank'sbalanced salt solution (HBSS) for 10 minutes, then incubated with 1ug/ml anti-human DKK1 antibody followed by a Rhodamine-conjugated rabbitanti-goat IgG F(ab′)₂ fragment (1:500 dilution, Jackson ImmunoResearch,West Grove, Pa.). Cytoplasmic immunoglobulin kappa or lambda lightchains were stained with FITC-conjugated goat anti-human kappa or lambdaIgG at 1:100 dilution (Vector Laboratories). All slides were examinedwith fluorescence optics on an Olympus BX60 epifluorescence microscopeand photographed at 1000× magnification.

[0269] DKK1 protein is synthesized by malignant and normal plasma cells.Serial sections from bone marrow biopsies of 30 multiple myeloma casesstained with antibodies against cytoplasmic immunoglobulin kappa orlambda light chain (cIg) and DKK1 revealed plasma cells expressed DKK1in a manner consistent with gene expression data (FIGS. 18A-D).Simultaneous two-color immunofluoresence microscopy on mononuclear cellsfrom bone marrow aspirates of 30 multiple myeloma patients and 9 normalhealthy donors was consistent with immunohistochemistry results, showingDKK1 protein expression exclusively in cIg-restricted plasma cells(FIGS. 2E-H). As seen in the multiple myeloma bone marrow aspirates,non-plasma cells in normal marrow did not express DKK1; however,approximately ⅓ to ½ of cIg-positive plasma cells expressed DKK1.Although DKK1 expression was not detected by microarray, DKK1expression, albeit low, could be detected in purified bone marrow plasmacells by real time RT-PCR (data not shown). TABLE 21 Myeloma PatientCharacteristics And Their Relationship To MRI Lesions Variable n/N % MRI= 1+ MRI = 0 P value Age ≧ 65 yr  23/169 14  17/132 (12.9%)  6/36(16.7%) 0.5869* Caucasian 147/169 87 113/132 (85.6%) 33/36 (91.7%)0.4163* Female  68/169 40  55/132 (41.7%) 13/36 (36.1%) 0.5472 Kappalight 104/165 63  79/128 (61.7%) 24/36 (66.7%) 0.5874 chain Lambda light 61/165 37  49/128 (38.3%) 12/36 (33.3%) 0.5874 chain IgA subtype 39/169 23  25/132 (18.9%) 14/36 (38.9%) 0.0120 B2M ≧ 4  60/169 36 47/132 (35.6%) 13/36 (36.1%) 0.9553 mg/L CRP ≧ 4  12/166  7  11/129(8.5%)  1/36 (2.8%) 0.4662* mg/L Creatinine ≧  19/169 11  16/132 (12.1%) 3/36 (8.3%) 0.7673* 2 mg/dL LDH ≧ 190  52/169 31  44/132 (33.3%)  8/36(22.2%) 0.2012 UI/L Albumin <  23/169 14  19/132 (14.4%)  4/36 (11.1%)0.7868* 3.5 g/dL Hgb < 10  40/169 24  31/132 (23.5%)  8/36 (22.2%)0.8736 g/dL PCLI ≧ 1%  23/150 15  18/119 (15.1%)  4/30 (13.3%) 1.0000*ASPC ≧ 109/166 66  82/129 (63.6%) 26/36 (72.2%) 0.3342 33% BMPC ≧104/166 63  79/129 (61.2%) 24/36 (66.7%) 0.5522 33% Cytogenetic  52/15633  45/121 (37.2%)  6/34 (17.6%) 0.0321 abnormali- ties CA13 or  33/5263  31/121 (25.6%)  3/34 (8.8%) 0.0365 hypodiploid Other CA  19/52 37 53/103 (51.5%) 16/32 (50.0%) 0.8855 FISH13  69/136 51 103/136 (75.7%)28/36 (77.8%) 0.7981 Osteopenia 131/173 76 1+ Lesions 137/173 79 by MRI3+ Lesions 108/173 62 by MRI 1+ Lesions 105/174 60 by X-ray 3+ Lesions 69/174 40 by X-ray

EXAMPLE 21

[0270] Correlation between Levels of DKK1 Protein in Bone Marrow Plasmaand the Presence of Bone Lesions

[0271] An enzyme-linked immunosorbent assay (ELISA) was used to measureDKK1 protein concentration in the bone marrow plasma. Nunc-ImmunoMaxiSorp surface microtiter plates were coated with 50 ul of anti-DKK1antibody at 1 ug/ml in 1×PBS, pH 7.2 at 4° C. overnight, and blockedwith 4% BSA. Bone marrow plasma from multiple myeloma, Waldenström's,and normal donors was diluted 1:50 in dilution buffer (1×PBS+0.1Tween-20+1% BSA). A total of 50 μl was loaded per well and incubatedovernight at 4° C., washed and incubated with biotinylated goatanti-human DKK1 IgG (R&D Systems) diluted to 0.2 ug/ml in dilutionbuffer, followed by addition of 50 μl of 1:10,000 dilution ofstreptavidin-horse radish peroxidase (Vector Laboratories) all accordingto manufacturer's recommendations. Color development was achieved withthe OPD substrate system (Dako) based on manufacturer's instructions.Serial dilutions of recombinant human DKK1 (R&D Systems) were used toestablish a standard curve. The cell line T293, which does not expressendogenous DKK1 and T293 with stably transfected DKK1 were used tovalidate the ELISA assay.

[0272] DKK1 protein concentration in the bone marrow plasma from 14normal healthy donors was 8.9 (S.D. 4.2) ng/ml. In contrast, the meanplasma concentration in 205 newly diagnosed multiple myeloma patientswas 28.37 (S.D. 54.45) ng/ml and the mean level in 9 Waldenström'sacroglobulinemia patients was 5.5 (S.D. 2.4) ng/ml. Both DKK1 geneexpression and DKK1 protein levels determined in 107 multiple myelomacases were strongly correlated (r=0.65, P<0.00001) (FIG. 19A). DKK1protein levels were correlated with bone lesions in 74 multiple myelomain which both measurements were available. DKK1 protein levels weresignificantly higher in patients with 1+ MRI versus those with no MRIlesions (OR: 1.98 [95% CI: 1.18, 3.32], P=0.012) (FIG. 19B), but onlymarginally significant for 1+ x-ray lesions versus no lesions (OR: 1.38[95% CI: 0.95, 2.00], P=0.09) (FIG. 19B). However, as observed for DKK1gene expression (see FIG. 17B), serum DKK1 protein levels candiscriminate patients with no MRI and no x-ray lesions from patientswith 1+ MRI and no x-ray lesions (OR: 7.46 [95% CI: 1.40, 39.82],P=0.0186), but cannot discriminate the 1+ MRI lesions and 1+ x-raylesions group from the 1+ MRI lesions/no x-ray lesions group (OR: 1.14[95%CI: 0.98, 1.32], P=0.5752) (FIG. 19B). These data show that DKK1proteins levels are elevated in MM bone marrow, that DKK1 geneexpression levels in multiple myeloma plasma cells correlate with DKK1protein levels in the serum, and that DKK 1 serum levels also correlatewith the presence of bone lesions.

EXAMPLE 22

[0273] Multiple Myeloma Marrow Serum Blocks In Vitro OsteoblastDifferentiation in a DKK1-Dependent Fashion

[0274] BMP-2 specifically converts the differentiation pathway of C2C12myoblasts into that of osteoblast lineage and BMP-2 induced osteoblastdifferentiation of mesenchymal precursor cells in vitro involvesWnt/β-catenin signaling. Thus, it is of interest to determine ifmultiple myeloma serum containing high levels of DKK1 could block BMP-2induced alkaline phosphatase (ALP) production by C2C12 cells.

[0275] C2C12 mesenchymal precursor cells (American Type Tissue Culture,Reston, Va.) were cultured in DMEM (Invitrogen, Carlsbad, Calif.)supplemented with 10% heat-inactivated fetal calf serum. BMP-2 inducedalkaline phosphatase activity in C2C12 cells was measured as previouslydescribed (Gallea et al., 2001; Spinella-Jaegle et al., 2001). Briefly,C2C12 cells were plated at 2×10⁴/cm² in 5% FCS and treated 24 hourslater by addition of 0.1 ug/ml BMP-2+10% normal serum, or 10% multiplemyeloma patient serum (resulting in final DKK1 concentration of at least15 ng/ml), or 10% multiple myeloma patient serum+anti-DKK1 antibody ornon-specific goat polyclonal IgG (Jackson ImmunoResearch) at a 5:1 molarratio to DKK1. After 5 days, ALP activity was determined in cell lysatesusing an Alkaline Phosphatase Opt Kit (Roche Molecular Biochemicals).Cell lysates were analyzed for protein content using the micro-BCA assaykit (Pierce, Rockford, Ill.). Each experiment was done in triplicate.

[0276] It was first showed that TGF-β (2.5 ng/ml) and recombinant humanDKK1 (12.5 ng/ml) could block ALP production after treatment with 0.1ug/ml of BMP-2 for 5 days (data not shown). ALP levels were undetectablein C2C12 cells grown in 5% FCS for 5 days (FIG. 20). C2C12 cells treatedwith BMP-2 and 10% serum from a normal donor induced ALP activity to amean of 0.72 mM/min/mg total protein. Cells treated with 10% serum fromtwo multiple myeloma cases (final DKK1 concentration 20 ng/ml and 16.8ng/ml) resulted in a significant reduction of ALP levels relative to thenormal control. However, co-incubation with DKK1 antibodies resulted ina significant increase in ALP levels. Co-incubation with a non-specificpolyclonal goat IgG did not have a significant affect on ALP levelsrelative to multiple myeloma serum alone. These data show that serumfrom multiple myeloma patients, but not normal donors, can block invitro osteoblast differentiation in a DKK1-dependent fashion.

EXAMPLE 23

[0277] Gene Expression Profiling Implicates Wnt Signaling in MultipleMyeloma

[0278] In addition to a possible role for FRZB and DKK1 in multiplemyeloma (MM) bone disease and DKK1 in drug induced MM cell apoptosis,additional components of the Wnt signaling system also show expressionperturbations in MM. Wnt signaling is mediated by the Frizzled(FZD1-FZD10) seven-transmembrane-span receptors alone or when complexedwith heterotrimeric G proteins or essential co-receptors LRP5/6.Intracellular signaling branches into either the canonical β-catenin-TCFpathway that activates target genes in the nucleus, the Wnt/Ca²⁺ pathwayor the planar cell polarity pathway which involves jun N-terminal kinase(JNK) and cytoskeleton modifications. With respect to the canonicalβ-catenin-TCF pathway, the absence of Wnt signal, which can be blockedby antagonist like FRZB and DKK1, results in β-catenin being sequesteredin a complex with Axin, adenomatous polyposis coli (APC), CKIα and/orCKIε, ανδGSK-3β. Both CKIα/ε and GSK-3β phosphorylate β-catenin therebytargeting the protein for ubiquination and degradation by theproteasome. In the presence of Wnts, disheveled (Dsh) blocks β-catenindegradation possibly by recruiting GBP/Frat1, which displaces GSK3β fromaxin resulting in β-catenin stabilization. Stabilized β-catenin migratesto the nucleus where it associates with TCF/LEF transcription factors.

[0279] Using the Affymetrix HuGenFL microarray, it has previously beenshown that, with respect to normal bone marrow PC, the TCF7 (TCF1)transcription factor and APC are down-regulated and up-regulated in MM,respectively (Zhan et al., 2002a). A recent analysis of 60 genesinvolved in or activated by WNT signaling in 222 MM and 45 normal bonemarrow PC using the second-generation U95Av2 microarray revealed thatTCF7 expression remained significant and represented the mostsignificantly down-regulated WNT signaling gene in MM. The APCsignificance was lost in this new analysis probably owing to a change inthe probe set interrogating the gene. The Wnt receptor FZD2 with a meanexpression level of 1034 in MM and 2657 in normal bone marrow PC,represented the second most significant down-regulated gene.Importantly, FZD2 has also been found to be significantly down-regulatedin MGUS and represents the only WNT signaling component foundderegulated in benign plasma cell dyscrasia. DKK1 and FRZB, with ratios(MM/normal bone marrow PC) of mean expression levels of 25 and 14respectively, represented by far the most significantly up-regulated WNTcomponents in this analysis.

[0280] WNT5A and WNT10B tended to show up-regulation and down-regulationin MM respectively (Zhan et al., 2002a). In the new analysis the meanexpression level for WNT5A was 217 (range 29 to 484) whereas the meanexpression level in MM was 1299 (range 16 to 10782). In contrast, themean expression level of WNT10B in normal bone marrow PC was 3239 (range1552 to 6105) and 1881 (range 481 to 15354) in MM. Thus, these twoligands show inverse relationships with respect to normal bone marrowPC. Furthermore, the two genes also tend to be inversely correlatedwithin the patient population.

[0281] Recent studies have shown that rat Wnt5a is a ligand for Rfz2(FZD2) and that binding of the ligand to receptor signals throughphosphodiesterase and cyclic GMP (Ahumada et al., 2002). Correlationanalysis of the expression of FZD2 and WNT5A in PC from 222 newlydiagnosed patients has shown that PC with low FZD2 tend to have highexpression of WNT5A and those with high FZD2 to have low WNT5A levels.One possible explanation for this phenomenon is that normal PC utilizesthe FZD2 signaling pathway and that down-regulation of FZD2, an apparentearly event in MM, results in a compensatory up-regulation of WNT5A.

[0282] Yamanaka et al. have recently shown that Wnt5a is also capable ofactivating JNK in cultured cells, and that the JNK pathway mediates theaction of Wnt5a to regulate convergent extension movements, a vertebratecorrelate of the planar cell polarity pathway of Drosophila (Yamanaka etal., 2002). Lisovsky and colleagues have also shown that XenopusFrizzled 8 (Xfz8) activates JNK and triggers rapid apoptotic cell deathin gastrulating Xenopus embryos (Lisovsky et al., 2002). They showedthat the apoptotic signaling was shared by a specific subset of Frizzledreceptors, was inhibited by Wnt5a, and occurred in a Dishevelled- and Tcell factor (TCF)-independent manner (Lisovsky et al., 2002). These datasuggests that WNT5A over-expression in MM may prevent apoptosis throughinhibition of JNK signaling. Hideshima et al. have demonstrated that theproteasome inhibitor PS-341 activates JNK, that this activation iscorrelated with MM cell apoptosis, and that blocking JNK activationabrogates PS-341-induced cell death (Hideshima et al., 2002). Thus itwill be interesting to determine if MM cells cultured in the presence ofexcess WNT5A demonstrate resistance to PS-341-induced apoptosis.

[0283] Previous studies on the effects of Wnts in hematopoiesis suggeststhat alteration in the ratio of specific Wnt ligands, especially Wnt5Aand Wnt10B, may have profound effects on normal bone marrow stem cellfunction (Austin et al., 1997). Brandon and colleagues have recentlyshown that treatment of hematopoietic progenitor cell-enriched bonemarrow cultures with soluble WNT11 or WNT5a inhibited macrophageformation and enhanced monocyte and RBC production (Van Den Berg et al.,1998). Thus, one of the consequences of elevated WNT5A in MM may be todrive progenitor cells to monocytes at the expense of macrophagedevelopment. This increased pool of monocytes, in the presence ofosteoclast differentiation factors such as RANKL, could theoreticallycontribute to increased osteoclast numbers.

[0284] Van Den Berg and colleagues have demonstrated that Wnt-5A,Wnt-2B, and Wnt-10B are expressed in fetal bone stromal cells, thatthese genes are expressed to varying levels in hematopoietic cell linesderived from T cells, B cells, myeloid cells, and erythroid cells, butthat only Wnt-5A is expressed in CD34(+)Lin-primitive progenitor cells(Van Den Berg et al., 1998). The authors also showed that the number ofhematopoietic progenitor cells is markedly affected by exposure tostromal cell layers expressing Wnt genes. Colony formation by cellsexpanded on the Wnt-expressing co-cultures was similar for each of thethree genes, indicating similar action on primitive progenitor cells;however, Wnt-10B showed differential activity on erythroid progenitors(BFU-E) compared with Wnt-5A and Wnt-2B (Van Den Berg et al., 1998).Co-cultures containing Wnt-10B alone or in combination with all threeWnt genes had threefold to fourfold lower BFU-E colony numbers than theWnt-5A- or Wnt-2B-expressing co-cultures (Van Den Berg et al., 1998).The relationship of Wnt signaling and stromal cell-hematopoietic celldevelopment has been furthered by studies showing that conditionedmedium containing Wnt-3a dramatically reduced the production of Blymphocyte and myeloid lineages, except for macrophages, in long-termbone marrow cultures grown on stromal cells (Brandon et al., 2000). Incontrast, the same conditioned medium did not affect the generation ofthese cells in stromal cell-free conditions. The authors took theseresults to suggest that Wnt proteins exert their effects through stromalcells and supported this hypothesis by showing that the same effectscould be mimicked by the expression of a stabilized form of beta-cateninin stromal cells (Yamane et al., 2001).

[0285] Evidence for a direct role of Wnt signaling in B-cell biology andB-cell disease is derived from recent studies showing that Wnt signalingrequires BCL9-mediated recruitment of pygopus to the nuclearβ-catenin-TCF complex (Kramps et al., 2002). GEP data from 222 newlydiagnosed MM and 45 normal bone marrow PC has revealed elevatedexpression of BCL9 in approximately one-third of MM (mean 2128; range758 to 4735) when compared to normal PC (mean 1447; range 394 to 2305).Reya et al. have shown that mice deficient for lymphocyte enhancerfactor-1 (LEF-1) exhibit defects in pro-B cell proliferation andsurvival in vitro and in vivo, that Lef1^(−/−) pro-B cells displayelevated levels of fas and c-myc transcription, and that Wnt proteinsare mitogenic for pro-B cells and that this effect is mediated by Lef1(Reya et al., 2000).

[0286] A role for WNT signaling in normal PC development is indicated byseveral observations. First, global GEP of human plasma celldifferentiation revealed that whereas WNT10B is not expressed in tonsilBC, it is turned on in both tonsil and bone marrow PC (Zhan et al.,2003). In contrast, FRZB was significantly down-regulated in thetransition of immature tonsil PC to mature bone marrow PC (Zhan et al.,2003). Secondly, Alexander et al. have demonstrated that syndecan-1(CD138/SDC1), a transmembrane heparan sulfate proteoglycan expressedexclusively on mature plasma cells within the hematopoietic lineage, isrequired for Wnt-1-induced mammary tumorigenesis in mice (Alexander etal., 2000), suggests that one possible function for syndecan-1 in PCbiology is to facilitate WNT signaling. Finally, a recent geneexpression study of mouse plasma cell differentiation has shown thatAxin and Frat1 are down-regulated in PC with respect to BC, whereas βcatenin and disheveled were equally expressed in both cell types(Underhill et al., 2003).

EXAMPLE 24

[0287] Gene Expression Profiling before and after Short-Term DrugTreatment to Identify Potential Mechanisms of Action

[0288] All current chemotherapy regimens for multiple myeloma (MM)treatment attempt to use combinations of multiple drugs affectingnon-overlapping, and non-cross resistant pathways. It is impossible todefine, in the context of these extensive combinations, the molecularmechanisms of action of individual components. Short-term serial geneexpression studies of tumor cells after single agent drug treatment mayprovide insight into the mechanisms of action, especially when combinedwith clinical response data. This knowledge, if confirmed, can lead tothe development of second-generation drugs with more effective responseprofiles and less toxicities. In addition, knowledge of pathways willallow development of drugs that target discrete points along pathwaysallowing use of complementary or synergistic drugs. Potential advantagesof performing invivo studies are that drug metabolism, tumor cell hostinteractions, and drug response correlations can be considered.

[0289] In an effort to gain insight into the mechanism of action ofvarious single-agent compounds, the inventors have performed baselineand 48-hour follow-up gene expression profiling (GEP) on patients beforeand after therapy with dexamethasone (n=20), thalidomide (n=18), thethalidomide derivative IMiD (n=15), or the proteasome inhibitor PS-341(n=11) (Shaughnessy et al., 2002).

[0290] Based on the t-test for differences in quantitative expressionchanges, dexamethasone induced the greatest change (147 genes P<0.0001;51 up and 96 down). PECAM1 was down-regulated in 20 of 20 cases andrepresented the most significantly altered gene (P=5.8×10−9) afterdexamethasone treatment. PECAM1 is an adhesion molecule that isup-regulated as PC mature from the tonsil to bone marrow stage ofdevelopment (Zhan et al., 2003), suggesting a critical role for thisprotein in PC adhesion to the bone marrow stroma. Thus, it is possiblethat down-regulation of PECAM1 may result in MM PC detachment and anincreased rate of spontaneous or chemotherapy induced apoptosis.

[0291] Virtually all (18 of 20) cases treated with dexamethasone alsoshowed down-regulation of the pro-angiogenic molecule VEGF and theanti-apoptotic molecule MCL1, both of which have been implicated in MMbiology. Since VEGF is up-regulated upon adherence of MM cells tostroma, it is not clear whether VEGF down-regulation is directly causedby dexamethasone or is a reflection of the down-regulation of PECAM1 anddetachment of the cells from stroma. Given that most patients initiallyrespond to dexamethasone treatment, but eventually develop resistance,performing GEP on patients after treatment with dexamethasone that areknown to be resistant to the drug might provide insight into themechanisms of action. For example, it may be found that dexamethasoneresistant MM PC may not down-regulate PECAM1 and VEGF after short-termtreatment.

[0292] Surprisingly, using the same t-test analysis, PS-341 only inducedsignificant changes in 9 genes (P<0.001; 2 up and 7 down). Thedown-regulation of the Cockayne syndrome 1 gene (CKN1) represented themost significant change (P=6×10−6) after PS-341 treatment. CKN1 encodesa WD repeat protein that interacts with CSB protein and a subunit of RNApolymerase II TFIIH and mutations of CKN1 are associated defectivestrand-specific repair of transcriptionally active genes. Thus, thedown-regulation of CKN1 by PS-341 may have negative effects on RNApolymerase II transcription and may explain the low number of alteredgenes relative to dexamethasone, thalidomide, and IMiD. A possiblemechanism of action of the drug may be to impair DNA damage repairthrough the down-regulation of CKN1. In such a scenario inactivation ofCKN1 could result in cells with germ line TP53 being driven into anapoptotic program rather than a DNA repair pathway. Such a scenario, iftrue, suggests that combining PS-341 with alkylating agents may havesynergistic effects.

[0293] IMiD or CC-5013, a potent thalidomide analog, induced changes in98 genes (P<0.001; 41 up and 57 down), whereas thalidomide inducedsignificant changes in 57 genes (P<0.001; 29 up and 28 down). Given thatIMiD and thalidomide are related molecules, it is speculated thatconsistent and common changes in gene expression influenced by bothdrugs might point to mechanisms of action. Seven genes, CROT, IL6, TPBG,ALB, PLSCR1 and, DKK1 were activated by both drugs with up-regulationafter IMiD treatment being more pronounced for all genes, attesting tothe higher potency of the derivative. Importantly, Dickkopf1 (DKK1), asecreted antagonist of Wnt signaling, was hyperactivated a median of125% in 14 of 18 patients treated with thalidomide and up-regulated amedian of 315% in 13 of 15 cases treated with IMiD. Virtually all MMcases not showing a hyperactivation of DKK1 after treatment with bothdrugs had little or no detectable expression in the baseline sample,suggesting that these cases had an inherent inability to activate DKK1expression. Along these lines it was also noted that whereas DKK1 wasexpressed in nearly 80% of newly diagnosed MM, virtually all cases of PCleukemia and plural effusions from end stage M M lack expression ofDKK1. Given that DKK1 is a direct target of p53 and p53 loss isfrequently seen in late stage MM, it is possible that the lack ofexpression and/or inability to activate DKK1 in MM may be due to loss ofp53. In fact, one of the cases of newly diagnosed MM that showed no DKK1at baseline or after 48 hr treatment with thalidomide was primaryrefractory to all interventions. The p53 status in this patient was notknown.

[0294] Of 648 genes exhibiting a greater than 50% (range 51% to 408%)change in median expression level after IMiD treatment, DKK1, at 315%increase ranked as the 4^(th) most significantly altered gene. In asimilar analysis after thalidomide treatment, DKK1 was ranked 13^(th)out of 217 genes. Conversely, although hyperactivated in 14 of 20patients, DKK1 only showed a median increase of 23%, was ranked1426^(th) and not represented in the list of 280 genes exhibiting anup-regulation of at least 50% (range 51% to 470%) by dexamthasone. Theeffects of PS-341 were even more skewed from the IMiD and thalidomidedata in that only 7 of 15 patient samples exhibited increases in DKK1and the median change was essentially undetectable at −1.60%. DKK1ranked 7,532 of 12,000 genes tested. Thus, these data provide the firstevidence that for the in vivo effects of drug treatment on DKK1expression, thalidomide and IMiD have powerful up-regulating effects;dexamethasone has an intermediate effect; and the proteasome inhibitorPS-341 has little or no effect.

[0295] It is important to determine if the drug has a direct activatingeffect or whether the increase really reflects the rapid andpreferential killing of cells not expressing DKK1 which would in turncreate a virtual up-regulation. Several points argue against the latterpossibility. First, DNA damaging agents like UV irradiation, H₂O₂,cisplatin, and BCNU activate DKK1 in vitro and this activationsensitizes cells to apoptosis. Second, it is unlikely that a massivecell kill occurred within 48 hours after in vivo administration.Finally, the inventos have recently found that in vitro treatment of thehuman MM cell line ARP1 with thalidomide, IMiD, and dexamethasoneresults in the activation of DKK1.

[0296] Given the fact that in vitro DNA damage triggers DKK1 expression,it is speculated that DNA instability present in MM PC may be a triggerof DKK1 activation in newly diagnosed, previously untreated cases of MM.Thus, DKK1 not only could be a major mediator of MM PC cell death afterdrug treatment, but also a mediator of apoptosis in these cells as aresult of endogenous DNA damage. Indirect support for a role for DKK1 inM M cell apoptosis comes from previous studies showing that 1) DKK1 isactivated by BMP-4, and 2) that treatment of MM cell lines and primaryMM samples with BMP-4 can inhibit proliferation and induce apoptosis.These data suggests that one possible mechanism of BMP-4 induced MM PCkilling is through the activation of DKK1.

[0297] DKK1 has been shown to be critical for limb morphogenesis duringvertebrate embryogenesis. DKK1 null mutant embryos show duplications andfusions of limb digits whereas forced over expression of DKK1 inembryonic limb buds inhibits limb outgrowth. The limb defects resultingfrom over expression of DKK1 have a strong resemblance to the limbdefects seen in thalidomide embryopathy. Thus, an intriguing implicationof the link between thalidomide and DKK1 activation is that disruptionof WNT signaling through DKK1 activation by thalidomide may be the longsought mechanism by which thalidomide causes such limb malformations.Interestingly, a link between DKK1 expression and MM-specific bonedefects has become apparent through the use of microarray profiling (seeabove).

[0298] Thus, in vivo monitoring of gene expression changes followingsingle agent drug treatment appears to be a feasible approach to studythe effect of treatment and molecular mechanisms of action. These typesof study have powerful advantages over in vitro studies of the samedesign, in that response, tumor cell host interactions, and non-tumorcell effects (variables that cannot be faithfully recapitulated in modelsystems) can be taken into account.

[0299] The following references are cited herein:

[0300] Ahumada et al., Signaling of rat Frizzled-2 throughphosphodiesterase and cyclic GMP. Science 298:2006-10 (2002).

[0301] Alexander et al., Syndecan-1 is required for Wnt-1-inducedmammary tumorigenesis in mice. Nat. Genet. 25:329-32 (2000).

[0302] Austin et al., A role for the Wnt gene family in hematopoiesis:expansion of multilineage progenitor cells. Blood 89:3624-35 (1997).

[0303] Brandon et al., WNT signaling modulates the diversification ofhematopoietic cells. Blood 96:4132-41 (2000).

[0304] Brown et al., Support vector machine classification of microarraygene expression data. UCSC-CRL 99-09, Department of Computer Science,University California Santa Cruz, Santa Cruz, Calif. (1999).

[0305] Chauhan et al., Identification of genes regulated byDexamethasone in multiple myeloma cells using oligonucleotide arrays.Oncogene 21:1346-1358 (2002).

[0306] Cheng et al., KDD Cup 2001. SIGKDD Explorations 3:47-64 (2000).

[0307] Cristianini and Shawe-Taylor, An Introduction to Support VectorMachines and other kernel-based learning methods. Cambridge UniversityPress (2000).

[0308] De Vos et al., Identifying intercellular signaling genesexpressed in malignant plasma cells by using complementary DNA arrays.Blood 98:771-80 (2001).

[0309] Eisen et al., Cluster analysis and display of genome-wideexpression patterns. Proc Natl Acad Sci USA 95:14863-14868 (1998).

[0310] Friedman et al., Learning Bayesian network structure from massivedatasets: the “sparse candidate” algorithm. Proceedings of theInternational Conference on Uncertainty in Artificial Intelligence(1999).

[0311] Furey et al., Support vector machine classification andvalidation of cancer tissue samples using microarray expression data.Bioinformatics 16:906-914 (2000).

[0312] Gallea et al., Activation of mitogen-activated protein kinasecascades is involved in regulation of bone morphogeneticprotein-2-induced osteoblast differentiation in pluripotent C2C12 cells.Bone 28:491-8 (2001).

[0313] Hideshima et al., Molecular mechanisms mediating anti-myelomaactivity of proteasome inhibitor PS-341. Blood (2002).

[0314] International Human Genome Sequencing Consortium, Initialsequencing and analysis of the human genome. Nature 409:860-921 (2001).

[0315] Kohavi, A study of cross-validation and bootstrap for accuracyestimation and model selection. Proceedings of the International JointConference on Artificial Intelligence (IJCAI) (1995).

[0316] Kramps et al., Wnt/wingless signaling requiresBCL9/legless-mediated recruitment of pygopus to the nuclearbeta-catenin-TCF complex. Cell 109:47-60 (2002).

[0317] Li and Wong, Model-based analysis of oligonucleotide arrays:expression index computation and outlier detection. Proc Natl Acad SciUSA 98:31-36 (2001).

[0318] Lisovsky et al., Frizzled receptors activate a novelJNK-dependent pathway that may lead to apoptosis. Curr. Biol. 12:53-8(2002).

[0319] Murphy, The Bayes Net Toolbox for Matlab. Computing Science andStatistics: Proceedings of the Interface, (2001).

[0320] Pe'er et al., Inferring Subnetworks from Perturbed ExpressionProfiles. Proceedings of the Ninth International Conference onIntelligentSystems for Molecular Biology (2001).

[0321] Reya et al., Wnt signaling regulates B lymphocyte proliferationthrough a LEF-1 dependent mechanism. Immunity 13:15-24 (2000).

[0322] Shaughnessy et al., High incidence of chromosome 13 deletion inmultiple myeloma detected by multiprobe interphase FISH. Blood96:1505-1511 (2000).

[0323] Shaughnessy et al., Gene expression profiling after short term invivo treatment identifies potential mechanisms of action of currentdrugs used to treat multiple myeloma. Blood 100:781a (2002).

[0324] Spinella-Jaegle et al., Opposite effects, of bone morphogeneticprotein-2 and transforming growth factor-beta1 on osteoblastdifferentiation. Bone 29:323-30 (2001).

[0325] Tarte et al., Generation of polyclonal plasma cells fromperipheral blood B cells: a normal counterpart of malignant plasmacells. Blood In press (2002).

[0326] Underhill et al., Gene expression profiling reveals a highlyspecialized genetic program of plasma cells. Blood (2003).

[0327] Van Den Berg et al., Role of members of the Wnt gene family inhuman hematopoiesis. Blood 92:3189-202 (1998).

[0328] Vapnik, Statistical Learning Theory. John Wiley & Sons (1998).

[0329] Venter et al., The sequence of the human genome. Science291:1304-51 (2001).

[0330] Yaccoby et al., Significant and consistent gene expressionchanges highlight the molecular consequences of myeloma and normal donorplasma cell interactions with osteoclasts. Blood 100: 2392a (2002).

[0331] Yamanaka et al., JNK functions in the non-canonical Wnt pathwayto regulate convergent extension movements in vertebrates. EMBO 3:69-75(2002).

[0332] Yamane et al., Wnt signaling regulates hemopoiesis throughstromal cells. J Immunol 167:765-72 (2001).

[0333] Zhan et al., Global gene expression profiling of multiplemyeloma, monoclonal gammopathy of undetermined significance, and normalbone marrow plasma cells. Blood 99:1745-1757 (2002a).

[0334] Zhan et al., Gene expression profiling of multiple myeloma plasmacells allows identification of potential molecular determinants of lyticbone disease. Blood 100: 782a (2002b).

[0335] Zhan et al., Gene expression profiling of human plasma celldifferentiation and classification of multiple myeloma based onsimilarities to distinct stages of late stage B-cell development. Blood101:1128-1140 (2003).

[0336] Any patents or publications mentioned in this specification areindicative of the levels of those skilled in the art to which theinvention pertains. Further, these patents and publications areincorporated by reference herein to the same extent as if eachindividual publication was specifically and individually indicated to beincorporated by reference.

1 2 1 20 DNA Artificial Sequence primer_bind primer IGJH2 1 caatggtcaccgtctcttca 20 2 22 DNA Artificial Sequence primer_bind primer MMSET 2cctcaatttc ctgaaattgg tt 22

What is claimed is:
 1. A method of gene expression profiling formultiple myeloma, comprising the steps of: isolating plasma cells fromindividuals with or without multiple myeloma; isolating nucleic acidsamples from said plasma cells; hybridizing said nucleic acid samples toa DNA microarray; and performing hierarchical clustering analysis ondata obtained from said hybridization, wherein said clustering analysiswill classify said individuals with or without multiple myeloma intodistinct subgroups.
 2. The method of claim 1, wherein said subgroups ofmultiple myeloma are MM1, MM2, MM3 and MM4.
 3. The method of claim 1,wherein said clustering analysis will identify genes with elevatedexpression in subsets of multiple myeloma patients.
 4. The method ofclaim 3, wherein said genes have accession number selected from thegroup consisting of M64347, U89922, X67325, X59798, U62800, U35340,X12530, X59766, U58096, U52513, X76223, X92689, D17427, L11329, L13210,U10991, L10373, U60873, M65292, HT4215, D13168, AC002077, M92934,X82494, and M30703.
 5. The method of claim 1, wherein said clusteringanalysis will identify genes with significantly different levels ofexpression in multiple myeloma patients as compared to normalindividuals, wherein said genes are potential therapeutic targets formultiple myeloma.
 6. The method of claim 5, wherein said potentialtherapeutic targets for multiple myeloma are genes that have accessionnumber selected from the group consisting of L36033, M63928, U64998,M20902, M26602, M21119, M14636, M26311, M54992, X16832, M12529, M15395,Z74616, HT2152, U97105, U81787, HT3165, M83667, L33930, D83657, M11313,M31158, U24577, M16279, HT2811, M26167, U44111, X59871, X67235, U19713,Y08136, M97676, M64590, M20203, M30257, M93221, S75256, U97188, Z23091,M34344, M25897, M31994, Z31690, S80267, U00921, U09579, U78525, HT5158,X57129, M55210, L77886, U73167, X16416, U57316, Y09022, M25077,AC002115, Y07707, L22005, X66899, D50912, HT4824, U10324, AD000684,U68723, X16323, U24183, D13645, S85655, X73478, L77701, U20657, M59916,D16688, X90392, U07424, X54199, L06175, M55267, M87507, M90356, U35637,L06845, U81001, U76189, U53225, X04366, U77456, L42379, U09578, Z80780,HT4899, M74088, X57985, X79882, X77383, M91592, X63692, M60752, M96684,U16660, M86737, U35113, X81788, HT2217, M62324, U09367, X89985, L19871,X69398, X05323, X04741, D87683, D17525, M64347, U89922, X67325, X59798,U62800, U35340, X12530, X59766, U58096, U52513, X76223, X92689, D17427,L11329, L13210, U10991, L10373, U60873, M65292, HT4215, D13168,AC002077, M92934, X82494, M30703, U9103 and NM012242.
 7. A method ofidentifying a group of genes that can distinguish between normal plasmacells and plasma cells of multiple myeloma, comprising the steps of:isolating plasma cells from individuals with or without multiplemyeloma; isolating nucleic acid samples from said plasma cells;hybridizing said nucleic acid samples to a DNA microarray; identifyingdifferential gene expression patterns that are statisticallysignificant; and applying linear regression analysis to identify a groupof genes, wherein said group of genes is capable of accuratediscrimination between normal plasma cells and plasma cells of multiplemyeloma.
 8. The method of claim 7, wherein said genes have accessionnumber HT5158, L33930, L42379, L77886, M14636, M26167, U10324, U24577,U35113, X16416, X64072, X79882, Z22970, and Z80780.
 9. The method ofclaim 7, wherein said group of genes is capable of accuratediscrimination between subgroups of multiple myeloma.
 10. The method ofclaim 9, wherein said genes have accession number X54199, M20902,X89985, M31158, U44111, X16416, HT2811, D16688, U57316, U77456, D13645,M64590, L77701, U20657, L06175, M26311, X04366, AC002115, X06182,M16279, M97676, U10324, S85655, and X63692.
 11. A method of diagnosisfor multiple myeloma, comprising the steps of: isolating plasma cellsfrom an individual; examining expression of a group of 14 genes withinsaid plasma cells, said 14 genes have accession numbers HT5158, L33930,L42379, L77886, M14636, M26167, U10324, U24577, U35113, X16416, X64072,X79882, Z22970, and Z80780; and performing statistical analysis on theexpression levels of said genes, wherein a statistically significantvalue of said analysis indicates that said individual has multiplemyeloma.
 12. The method of claim 11, wherein the expression of said 14genes is examined at the nucleic acid level or protein level.
 13. Amethod of diagnosis for subgroups of multiple myeloma, comprising thesteps of: isolating plasma cells from an individual; examiningexpression of a group of 24 genes within said plasma cells, said 24genes have accession numbers X54199, M20902, X89985, M31158, U44111,X16416, HT2811, D16688, U57316, U77456, D13645, M64590, L77701, U20657,L06175, M26311, X04366, AC002115, X06182, M16279, M97676, U10324,S85655, and X63692; and performing statistical analysis on theexpression levels of said genes, wherein a statistically significantvalue of said analysis provides diagnosis for subgroups of multiplemyeloma.
 14. The method of claim 13, wherein the expression of said 24genes is examined at the nucleic acid level or protein level.
 15. Amethod of treatment for multiple myeloma, comprising the step of:inhibiting expression of a gene that has accession number selected fromthe group consisting of U09579, U78525, HT5158, X57129, M55210, L77886,U73167, X16416, U57316, Y09022, M25077, AC002115, Y07707, L22005,X66899, D50912, HT4824, U10324, AD000684, U68723, X16323, U24183,D13645, S85655, X73478, L77701, U20657, M59916, D16688, X90392, U07424,X54199, L06175, M55267, M87507, M90356, U35637, L06845, U81001, U76189,U53225, X04366, U77456, L42379, U09578, Z80780, HT4899, M74088, X57985,X79882, X77383, M91592, X63692, M60752, M96684, U16660, M86737, U35113,X81788, HT2217, M62324, U09367, X89985, L19871, X69398, X05323, X04741,D87683, D17525, M64347, U89922, X67325, X59798, U62800, U35340, X12530,X59766, U58096, U52513, X76223, X92689, D17427, L11329, L13210, U10991,L10373, U60873, M65292, HT4215, D13168, AC002077, M92934, X82494,M30703, U9103 and NM012242.
 16. A method of treatment for multiplemyeloma, comprising the step of: increasing expression of a gene thathas accession number selected from the group consisting of L36033,M63928, U64998, M20902, M26602, M21119, M14636, M26311, M54992, X16832,M12529, M15395, Z74616, HT2152, U97105, U81787, HT3165, M83667, L33930,D83657, M11313, M31158, U24577, M16279, HT2811, M26167, U44111, X59871,X67235, U19713, Y08136, M97676, M64590, M20203, M30257, M93221, S75256,U97188, Z23091, M34344, M25897, M31994, Z31690, S80267, U00921.
 17. Amethod of developmental stage-based classification for multiple myeloma,comprising the steps of: (a) isolating plasma cells and B cells fromnormal individuals; (b) isolating nucleic acid samples from said plasmacells and B cells; (c) hybridizing said nucleic acid samples to a DNAmicroarray; (d) performing hierarchical clustering analysis on dataobtained from said hybridization, wherein said clustering analysis willidentify genes that classify said plasma cells and B cells according totheir developmental stages; (e) isolating multiple myeloma plasma cellsfrom individuals with multiple myeloma; (f) isolating nucleic acidsamples from said multiple myeloma plasma cells; (g) hybridizing nucleicacid samples of (f) to a DNA microarray; (h) performing hierarchicalclustering analysis on data obtained from (d) and (g), wherein saidclustering analysis will classify said multiple myeloma plasma cellsaccording to the developmental stages of normal B and plasma cells. 18.The method of claim 17, wherein said plasma cells are isolated from anorgan selected from the group consisting of tonsil, bone marrow, mucoaltissue, lymph node and peripheral blood.
 19. The method of claim 17,wherein said B cells are isolated from an organ selected from the groupconsisting of tonsil, bone marrow, lymph node and peripheral blood. 20.A method of discriminating normal, hyperplastic and malignant plasmacells, comprising the steps of: obtaining gene expression data by DNAmicroarray; and performing statistical analysis on said data by a methodselected from the group consisting of logistic regression, decisiontrees, ensembles, naive bayes, bayesian networks and support vectormachines, wherein said analysis would discriminate among normal,hyperplastic and malignant plasma cells.
 21. The method of claim 20,wherein said analysis would identify a gene with altered expressionbetween normal and malignant plasma cells.
 22. A method of controllingbone loss in an individual, comprising the step of inhibiting theexpression of the DKK1 gene (accession number NM012242) or the activityof the protein expressed by the DKK1 gene.
 23. The method of claim 22,wherein said DKK1 gene expression is inhibited by anti-senseoligonucleotides or by anti-DKK1 antibodies.
 24. A method of controllingbone loss in an individual, comprising the step of administering to saidindividual a pharmacological inhibitor of DKK1 protein.
 25. The methodof claim 24, wherein said individual has a disease selected from thegroup consisting of multiple myeloma, osteoporosis, post-menopausalosteoporosis and malignancy-related bone loss.
 26. The method of claim25, wherein said malignancy-related bone loss is caused by breast cancermetastasis to the bone or prostate cancer metastasis to the bone.